Automated systems and methods for obtaining, storing, processing and utilizing immunologic information of individuals and populations for various uses

ABSTRACT

A system and method for assessing the immunological status of one or more individuals in a patient population is presented. The method includes establishing a database comprising a plurality of records of information each representative of the immune status of an individual in the population, each of said records including (1) current information from one or more assays for the presence of a biochemical, and (2) individual specific information comprising one or more of said individual&#39;s medical history, said individual&#39;s doctors&#39; observations and historical, demographic, lifestyle, and familial information relating to said individual. The method further includes processing the information in said database to find trends or patterns relating to the immune status of individuals in said patient population; and using the said trends or patterns as part of a health care related decision making process. In exemplary embodiments of the present invention, processing the information in the database includes generating a list of correlations between variables or fields in the database. The correlations in the list can be further refined automatically, and a set of hypotheses or literature citations can be linked to the final correlations. The correlations, the processing, their associated hypotheses can then be reported to a user or automatically fed into another system component to generate a medical or health related decision. In exemplary embodiments of the present invention, a first assay panel containing a plurality of cytokine assays can be administered and the results processed. Based on automatic analyses of the cytokine data, a second tier or set of assays can be run on the same individual. The cytokine assay results being used to inform the contents of a second assay panel.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. Utility patentapplication Ser. No. 11/796,727, filed on Apr. 27, 2007, and which waspublished as U.S. Patent Application Pub. No. 2008-0091471 A1, thedisclosure of which is hereby incorporated herein by reference. Thisapplication also claims the benefit of U.S. Provisional PatentApplication Ser. No. 61/002,704, filed on Nov. 8, 2007, the disclosureof which is also hereby incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to individualized health care, immunologyand medical informatics, and more particularly to automated systems andmethods for acquiring, storing, processing and utilizing immunologic andother information of individuals and populations for decision making invarious public health, medical and health care related applications.

BACKGROUND OF THE INVENTION

Personalized medicine is considered by many to be the wave of thefuture. A personalized medicine approach seeks to identify whether agiven individual needs a given treatment or intervention prior toadministering it, rather than relying on “standards” representing anaverage person in a group or population.

This approach is based on the well known fact that some individuals in ademographic population have naturally low or naturally high values whichare not best measured against a statistical mean for the demographicpopulation, but against that individual's own measured history.

Determination of the immune status of individuals to, for example,vaccine-preventable diseases requires an assay system that can detectantibodies that may be present at very low levels, especially whennatural or vaccine exposure may have been many years previously. Inaddition, such an assay system could be used more generally to assess anindividual's immune competence at different stages of that individual'slife, as well as to also measure the vaccine status of individuals withvarying special needs and requirements (e.g., military personnel ortravelers).

What is thus needed in the art is a system and method for measuring andprocessing immunologic information of individuals and populationsthrough various points in time of their lives so as to better track eachindividual's immune status and make appropriate diagnostic, prophylacticand therapeutic recommendations.

What is further needed in the art is a supporting structure toconveniently store the results of such screenings for easy access andprocessing, for data mining purposes as well as for use in a variety ofcommercial, research and governmental applications where a knowledge ofthe immunological indicia of customers, subjects and citizens can createefficiencies and optimizations, as well as allow for the exploitation ofcommercial opportunities and improve the quality of life.

SUMMARY OF THE INVENTION

A system and method for assessing the immunological status of one ormore individuals in a patient population is presented. The methodincludes establishing a database comprising a plurality of records ofinformation each representative of the immune status of an individual inthe population, each of said records including (1) current informationfrom one or more assays for the presence of a biochemical, and (2)individual specific information comprising one or more of saidindividual's medical history, said individual's doctors' observationsand historical, demographic, lifestyle, and familial informationrelating to said individual. The method further includes processing theinformation in said database to find trends or patterns relating to theimmune status of individuals in said patient population; and using thesaid trends or patterns as part of a health care related decision makingprocess. In exemplary embodiments of the present invention, processingthe information in the database includes generating a list ofcorrelations between variables or fields in the database. Thecorrelations in the list can be further refined automatically, and a setof hypotheses or literature citations can be linked to the finalcorrelations. The correlations, the processing, their associatedhypotheses can then be reported to a user or automatically fed intoanother system component to generate a medical or health relateddecision. In exemplary embodiments of the present invention, a firstassay panel containing a plurality of cytokine assays can beadministered and the results processed. Based on automatic analyses ofthe cytokine data, a second tier or set of assays can be run on the sameindividual. The cytokine assay results being used to inform the contentsof a second assay panel.

BRIEF DESCRIPTION OF THE DRAWINGS Section I Figures

FIG. 1 depicts a generalized exemplary process flow according toexemplary embodiments of the present invention;

FIG. 2 depicts an exemplary system overview according to exemplaryembodiments of the present invention;

FIG. 2A depicts an alternate exemplary system overview according toexemplary embodiments of the present invention;

FIG. 2B depicts yet another alternate exemplary system overviewaccording to exemplary embodiments of the present invention;

FIGS. 3 and 4 depict various exemplary configurations for assaying apatient sample according to an exemplary embodiment of the presentinvention;

FIG. 5 depicts a detailed system diagram according to an exemplaryembodiment of the present invention;

FIG. 5A depicts a detailed system diagram according to an alternateexemplary embodiment of the present invention;

FIG. 5B depicts T helper cell commitment towards specific lineages;

FIG. 5C depicts an exemplary Th1/Th2 Paradigm model as it existed circa2000;

FIG. 5D depicts an exemplary evolving Th1/Th2/Th17/Treg paradigm, whichnow includes arms that recognize the importance of Th17 and Treg cells;

FIG. 5E depicts an exemplary model illustrating how Treg-mediatedcontrol of CD80/CD86 expression may control the threshold of antigenrecognition, crucial for preventing the activation of low avidityself-reactive T cells that are below the cut-off imposed during thymicselection;

FIG. 5F depicts a model of the development of the immune response inschitosome infection;

Section II Figures

FIG. 6 depicts exemplary assay results in an exemplary databaseaccording to the present invention;

FIG. 7 depicts exemplary diagnostic module recommendation typesaccording to an exemplary embodiment of the present invention;

FIG. 8 illustrates an exemplary perceptron network which implements arule for a normal individual using as inputs the results of an exemplarymenigicoccal diagnostic panel;

FIG. 8A illustrates the exemplary perceptron network of FIG. 8implementing a similar rule for an abnormal individual;

FIG. 9 depicts an XML representation of the exemplary perceptronnetworks of FIGS. 8 and 8A;

FIG. 10 depicts an exemplary symbology for diagnostic goals which can beused to articulate diagnostic goals in an exemplary embodiment of thepresent invention;

FIG. 11 illustrates exemplary diagnostic goals using the symbology ofFIG. 10;

FIG. 12 illustrates an exemplary database schema for patient informationaccording to an exemplary embodiment of the present invention;

FIG. 13 illustrates an exemplary database schema for visit informationaccording to an exemplary embodiment of the present invention;

FIG. 14 illustrates an exemplary database schema for test resultsaccording to an exemplary embodiment of the present invention;

FIG. 15 depicts exemplary patient age intervals used in an exemplarydatabase according to an exemplary embodiment of the present invention;

FIG. 16 is a plot of an exemplary female antibody comparison over anumber of years according to an exemplary embodiment of the presentinvention.

FIG. 17 is a plot of an exemplary comparison of two individual females,one vaccinated and one not vaccinated, according to an exemplaryembodiment of the present invention;

FIG. 18 is a plot of exemplary antibody levels in a compliment-deficientindividual according to an exemplary embodiment of the presentinvention;

FIG. 19 is a plot of exemplary antibody levels in a healthy individualaccording to an exemplary embodiment of the present invention;

FIG. 19A is an example SQL query according to an exemplary embodiment ofthe present invention; and

FIG. 19B is a table illustrating the correlation among antibody levelsin an exemplary female population according to an exemplary embodimentof the present invention;

FIGS. 20 through 20F illustrate exemplary data mining results obtainedfrom operating on an exemplary database according to an exemplaryembodiment of the present invention;

FIG. 21A illustrates an exemplary pattern detection process flowaccording to an exemplary embodiment of the present invention;

FIG. 21B illustrates an exemplary pattern detection process flow withhypothesis generation according to an exemplary embodiment of thepresent invention;

FIG. 21C illustrates an exemplary automatic pattern detection processflow according to an exemplary embodiment of the present invention;

FIGS. 21D-1 through 21D-37 illustrate automated data mining protocolsaccording to an exemplary embodiment of the present invention;

FIGS. 21D-38 through 21D-40 respectively illustrate exemplary algorithmsfor Hepatitis A Virus (HAV), Hepatitis B Virus (HBV), and Hepatitis CVirus (HCV) Testing according to an exemplary embodiment of the presentinvention;

FIGS. 21E-1 through 21E-12 depict exemplary data analysis resultsobtained using an exemplary embodiment of the present invention;

FIGS. 21F-1 through 21F-6 depict the results of predictive models builtusing cytokine data according to an exemplary embodiment of the presentinvention;

FIGS. 21G-1 through 21G-12 depict the results of running an exemplarypatient population rule mining protocol according to an exemplaryembodiment of the present invention;

FIGS. 21H-1 through 21H-10 depict the results of running an exemplaryindividual patient vaccine recommendation protocol according to anexemplary embodiment of the present invention;

FIG. 21I is an exemplary output from an exemplary automated data miningprotocol according to an exemplary embodiment of the present invention,segmenting an exemplary database by Region of origin, Sex and thecytokine assay IFN-gamma;

Section III Figures

FIG. 22 is a process flow diagram for use in a healthcare managementembodiment according to the present invention;

FIG. 23 is a subset of the process flow depicted in FIG. 22;

FIG. 24 is an alternative process flow chart for healthcare managementaccording to the exemplary embodiment of the present invention;

FIG. 24A is a more detailed process flow chart similar to that of FIG.22;

FIG. 25 is an alternative process flow chart for managing healthcareaccording the exemplary embodiment of the present invention;

FIG. 25A is the process flow chart of FIG. 25 with an additionaloptional element;

FIG. 26 is an alternative process flow chart for managing healthcareaccording to the exemplary embodiment of the present invention;

FIG. 26A is an alternative version of the process flow of FIG. 26 withgreater detail;

FIG. 27 is a process flow chart for cervical cancer prevention accordingto the exemplary embodiment of the present invention;

FIG. 28 is a process flow chart for managing the care of women ofchildbearing age according to the exemplary embodiment of the presentinvention;

FIG. 29 is a process flow chart for an exemplary “Vaccine-O-Mat”application according to an exemplary embodiment of the presentinvention;

FIG. 29A is a system diagram of entities involved in the vaccinedistribution application according to an exemplary embodiment of thepresent invention;

FIG. 29B illustrates the necessary connectivity for the vaccinedistribution application illustrated in FIG. 29A;

FIG. 29C is the connectivity displayed in that FIG. 29B recast by use ofan interapplication connectivity provider according to an exemplaryembodiment of the present invention;

FIG. 30 is an exemplary flow chart for use in a life insuranceoptimization application according to an exemplary embodiment of thepresent invention;

FIG. 31 is an exemplary process flow chart for use in animmunosenescence management application according to an exemplaryembodiment of the present invention;

FIG. 32 is an exemplary process flow chart for a disaster managementapplication according to an exemplary embodiment of the presentinvention;

FIG. 33 is an alternative process flow chart for the psychologicalaspects of disaster response for a disaster response applicationaccording to an exemplary embodiment of the present invention;

FIG. 34 depicts exemplary process flow in an immunogenicity discoveryapplication according to an exemplary embodiment of the presentinvention;

FIG. 35 illustrates components of an exemplary two-sided marketapplication according to an exemplary embodiment of the presentinvention; and

FIG. 36 illustrates components of an exemplary drug hypersensitivitytwo-sided market application according to an exemplary embodiment of thepresent invention.

It is noted that the U.S. version of this patent or application filecontains at least one drawing executed in color. Copies of this patentor patent application publication with color drawings will be providedby the U.S. Patent Office upon request and payment of the necessaryfees.

TABLE OF CONTENTS Page CROSS-REFERENCE TO RELATED APPLICATIONS: 1TECHNICAL FIELD: 1 SUMMARY OF THE INVENTION: 2 BRIEF DESCRIPTION OF THEDRAWINGS: 2 DETAILED DESCRIPTION OF THE INVENTION: 16 EXEMPLARY ASSAYPANELS 24 A. COLLEGE STUDENT DIAGNOSTIC PANELS 30 1. MeningococcalDiagnostic Panel 30 2. Sexually Transmitted Diseases Assay Panel 31 3.Persistent Immunity Induced by Childhood Vaccines 31 B. ADULT DIAGNOSTICPANELS 32 1. Measurement of Immunity Induced By Vaccines for MilitaryPersonnel 32 2. ImmunoScore Measurement of Vaccine-Induced Immunity forTravelers 33 3. Cytokine Measurement in ImmunoScore 33 4. Quantitation80 C. IMMUNOSCORE EXEMPLARY SUPERPANELS 85 1. ImmunoScore DiagnosticPanel and Preventive Therapy for Autoimmune Disease 85 2. ImmunoScoreDiagnostic Panel: Aging, Longevity, Cancer and Human Cytomegalovirus 89D. EXEMPLARY IMMUNOSCORE SUPERPANELS 102 1. Middle School StudentImmunoPrint Super Diagnostic Panel 102 2. Exemplary ImmunoScoreDiagnostic Panels for Women of Child-Bearing Years 110 EXEMPLARYIMMUNOSCORE SYSTEM DATABASES 116 A. General Overview 116 B. ExemplaryIllustrative Database 125 1. Overall Description 125 2. Impact of DataMining 131 3. Diagnostic Module 134 4. Data Mining Module 144 C.Exemplary Canadian Immigrant Project Database Used To Illustrate DataMining and Hypothesis Generation 150 D. Data Mining Analyses andConclusions 159 1. Linear regression analysis—correlation coefficients160 2. Geometric mean values 161 3. Percent support between variables163 4. Possible Conclusions 165 E. PATTERN DETECTION AND HYPOTHESISGENERATION 167 1. Initial Exemplary Analysis: Data Mining Steps 178 F.AUTOMATED DATA MINING 180 1. Exemplary Software Development Environment181 2. Client-Server Computing 182 3. Third-party Applications 183 4.Extending Pipeline Pilot 183 5. Integrating Protocols 184 6. Data MiningTool 184 7. Single Patient Vaccine Recommendations 189 8. PatientPopulation Rule Mining 193 9. Age Binned with Differences 195 10.Automated Data Mining 198 G. EXEMPLARY INTERNAL HYPOTHESIS DATABASE 206H. EXPLANATION AND BASIS OF EXEMPLARY RULES CREATED FOR PROCESSING cipDATABASE 218 I. INTERPRETATION OF CERTAIN RESULTS OF AUTOMATED DATAMINING 233 J. EXTENSION OF DATABASE AND AUTOMATIC DATA MININGFUNCTIONALITY 238 K. EXEMPLARY ANALYSES PERFORMED ON CIP DATABASE 240 L.EXEMPLARY RESULTS USING DATA MINING PROTOCOLS ON CIP DATABASE 249 USESOF IMMUNOSCORE INFORMATION AND AUTOMATED DATA MINING RESULTS IN VARIOUSCOMMERCIAL, RESEARCH 253 AND GOVERNMENTAL CONTEXTS A. Health InsuranceUnderwriting and Management 253 B. Health Care/Health Insurance CreditExchanGe 265 C. Veterans Health Care Management (Variant of Health Care)269 D. Socialized Medicine Management 270 E. Supplemental Insurance(AFLAC Model) 271 F. ImmunoScore and the Wellness Industry 273 G. Womenof Childbearing Age/Screening of Pregnant Women 277 H.Vaccine-o-Mat/Vaccine Distribution Network 279 I. Consumer Accessibilityto Immunologic Information 283 J. Immunoscore Connectivity ViaInterapplication Translator/Data Integrator 284 K. ImmunologicInformatics Based Life Insurance Underwriting 285 L. Diagnosing andManaging Immunosenescence in the Elderly 289 M. Frozen Storage of NaiveImmune Cells (IRP Considerations) 301 N. Vaccine Use Outcome/Design 303O. Research Services 303 P. Immigration Consulting 304 Q. DisasterSurvivors: Immunizations, Recovery, Prognosis and Treatment 308 R.Monitor Adoptive Immunotherapy/Transplants 311 S. Elective Surgery 311T. Services to Charitable Foundations Promoting Immunological Well Being312 U. Discovery of Unwanted Immunogenicity of Therapeutics 313 V.Two-Sided Market Applications 316 W. Drug Hypersensitivity 329 X. HealthCare Transparency and Competition 339 1. Consistent high quality 339 2.Lower cost—follows from high quality 339 3. Available to all—forethical, political, systemic, and business reasons, health care must beavailable to everyone 340 4. Single model—every provider in the systemmust compete to offer the best product at the best price 340 5. Shapedby market forces—the consumer market has the sustained systemic power tobring consumers more for less 340 6. Practical—the solution must arisefrom present realities 340 7. Progressive—dramatic change can not occurall at once 340 8. Self-reinforcing—as any part of the health caresystem moves toward a new reality, that movement must allow andencourage 340 other parts to move forward as well Y. User Access ViaData Networks and On-line Advertising 350 Z. Prophilatic therapiesduring surgery 353 AA. Contraindications for biological activetheraputics 353 WHAT IS CLAIMED 367

Applicant notes that the TOC is defective, especially as regards SectionI. Applicant reserves the right to amend the Specification to correctthe TOC via Preliminary Amendment.

DETAILED DESCRIPTION OF THE INVENTION General Overview

In what follows, systems and methods of the present invention will beoften referred to as the “ImmunoScore” system, method and/or database,as the case may be. “ImmunoScore” is a trademark and/or service markcurrently envisioned by the assignee hereof to be utilized in connectionwith exemplary embodiments of the present invention.

The present invention is directed to the collection, processing, and useof immunologic information. Immunologic information is to be understoodin a broad sense, including any information which may be useful as anindicator of any immunological function of a mammalian body. Morespecifically, the present invention includes acquiring information thatis indicative of the immune status of an individual, processing thatinformation, storing the raw information as well as the outputs from theprocessing stage, and of that information at various times and invarious ways to recommend various actions such as prophylactic orfurther diagnostic interventions, or abstention from action, forindividuals or population. The present invention exploits a number ofadvances in technology as well as advances in how people think aboutmedical treatment. In exemplary embodiments of the present invention, anumber of immunological or immunological related (in a broad sense)assays can be administered to an individual. Using modern technologysuch as, for example, the M1M Analyzer marketed by BioVeris™ Corporationof Gaithersburg, Md., one can run a large number of assays, such as, forexample 20, 40 or 60, and obtain results therefrom in a relatively shortperiod of time. Moreover, these assay results can be stored in a memory,either locally or at one or more central servers or in associateddatabases, and can be operated upon by various algorithms or rules whichcan generate information as to that individual's immune status as wellas recommendations for further augmenting that immune status or takingfurther action in response to the information acquired, from the assaysand their processing. This information can be used in a variety ofcommercial, research, and healthcare contexts. Thus a variety ofbusiness methods or opportunities can be created or facilitated usingthe information obtained according to the methods of the presentinvention.

The present invention is described in three distinct sections. The firstsection describes the scientific background and motivation for creatingvarious assay panels to be administered, singularly or in combinationwith other assay panels, to different individuals in differentpopulations at different times in each individual's life cycle. Thisdiscussion culminates in suggested or exemplary assay super panels whichcan be administered in various contexts to various individuals.

A second section describes how information obtained from results of theadministered assays can be stored, processed, and utilized. Thisdiscussion comprises, inter alia, a description of an exemplary databasein which (i) results from numerous assays can be stored along with (ii)individual-specific information and (iii) the outputs of variousalgorithms which operate upon the assay results of that individual. Thissection also presents an exemplary database upon which immunologic datamining was performed according to the techniques of the presentinvention, and summarizes interesting and illustrative results form thatexercise.

In a third and final section, a variety of business and commercialmethods are described in which information from the assay panels asstored in the database and further processed can be used to increasebusiness efficiencies, create new markets and opportunities, and/orprovide useful tools for research and development.

Before describing each of these three areas in detail, a brief overviewof a generalized method and system according to exemplary embodiments ofthe present invention is presented with reference to FIGS. 1, 2, 2A and2B.

FIG. 1 depicts an exemplary process flow according to an exemplaryembodiment of the present invention. Beginning at 101, an assay or panelof assays can be conducted on a biological sample, e.g., blood, urine,etc., which has been taken from an individual. Such individual cansimply be an individual or he or she can be a member of a population orsub-population whose immunologic informatics are of use to some entityor enterprise. For example, the individual could be an insured of ahealth insurance company that is using the techniques of the presentinvention to efficiently manage the healthcare of its insureds so as tominimize costs. Or, alternatively, such an individual could be animmigrant whose vaccination history is unknown but whose immune statusis of interest to his new country's immigration service. Such exemplaryembodiments are described more fully below in Section III.

In FIG. 1, at 102 the results of the assay or assays conducted at 101can be obtained, and at 103 there can be an optional step of analyzingthe assay results locally. In exemplary embodiments of the presentinvention assays can be conducted and read in a variety of assay readingdevices. There are many assays available using known techniques. Some ofthem are more sophisticated and some less sophisticated. In exemplaryembodiments of the present invention, an assay reading device can, forexample, obtain results at 102, store those results and analyze themlocally, for example, in a processor communicably connected to the assayreading device. Alternatively, if only raw assay results are obtainedfrom a less sophisticated technology, those results can, for example, besent over a data network and stored in a database record. This isillustrated at 104. At 105, the results can be analyzed by accessing theparticular record associated with the particular individual to whom theassay panel or panels were administered at a given time. Such analysiscan involve a variety of algorithms ranging from a simplistic look atquantity of antibodies per defined unit of blood or other bodily fluid,or it can also, for example, include a complex analysis where a varietyof assay results are input and combined in linear and non-linear ways toproduce some metric of immunologic significance. Such algorithms aredescribed more fully below in Section II. Finally, at 106, based on theresults of the above described analysis, recommendations can begenerated. Such recommendations can include, for example, that theindividual obtain one or more vaccines, that the individual beadministered prophylactic therapies to boost his or her immune system,or that the individual be administered gene therapy to correct thegenetic defect which places him or her at risk of communicating acertain disease or condition, to name a few.

In general, in many exemplary embodiments according to the presentinvention process flow will be equivalent to or substantially similar tothe process flow depicted in FIG. 1. In each of those exemplaryembodiments, one or more panels of assays can be conducted with respectto one or more individuals. Results can be obtained, stored andanalyzed, and based on such analysis, recommendations for action (orinaction, such as, for example, in cases of over-vaccination, asdescribed above) can be recommended.

FIG. 2 is an exemplary generalized system diagram which correlates tothe generalized method depicted in FIG. 1. With reference to FIG. 2,there can be seen a number of assay devices 201. These assay devicesinclude one or more assay panels which have been conducted with respectto an individual or individuals and for which results have beenobtained. The results obtained from the assay devices can, as describedin connection with the generalized method in FIG. 1, be locally analyzedat each assay device, provided that such assay device has a dataprocessor and memory and the results can be stored locally at the assaydevice. Alternatively, the assay device results can, for example, becommunicated over a data network 202 to a central processor 204 andstored in a central database 203. The central processor 204 can accessthe records which it has received and analyze them by implementing anumber of analytic algorithms as described more fully below.

Central processor 204, based on its analysis, can generaterecommendations based on decision trees and criteria embedded in thevarious analytic algorithms it implements.

These recommendations can be displayed locally at the central processorat display 205 and can there be printed in a tangible medium fordistribution to interested persons. Alternatively, the central processor204 can, for example, send the results of its analysis over a datanetwork to various users who can access the results at user terminals210.

FIG. 2A presents an alternative generalized system diagram similar toFIG. 2. However, as can be seen in FIG. 2A, there is an additionaldatabase, the business rules database 220, communicably connected tocentral processor 204. In such an exemplary system the central processorcan implement algorithms to operate on stored assay data which can, forexample, also take as inputs various business rules in generating adecision regarding a recommendation. For example, as described morefully below in Section III, an exemplary embodiment of the presentinvention can be utilized to help a health insurance underwriter manageits population of insureds. There can, for example, be an annual orsemi-annual requirement of all insureds to have assays for variousimmunological components conducted on their blood or other bodilyfluids. After analysis of the results of such assays, an insurancecompany can determine whether a particular insured is susceptible to oneor more given diseases or other ailments which would result in increasedexpenditures for medical treatment. The insurance company could thendecide if it was not more economical to require the insured to undergocertain prophylactic treatments, such as, for example, vaccines orimmune system boosting therapies, etc., where the cost of suchprophylactic therapies is less than, as determined by some userdetermined factor, the expected exposure for medical care if the insuredcontracts one or more of the diseases or ailments to which he or she issusceptible.

In such context, there would need to be a number of business rules wheresuch user defined quantities, threshold levels, cost functions ormetrics, figures of merit, expected risks, etc., can be input andarticulated or incorporated in a number of rules. Such rules can then betaken into account by the central processor in implementing algorithmswhich take as inputs data from business rules database 220 as well as aprimary ImmunoScore database 203.

FIG. 2B presents an alternative generalized system diagram similar toFIGS. 2A and 2B. However, as can be seen in FIG. 2B, there is shown yetanother additional database, a hypothesis and rules database 250,communicably connected to central processor 204. In such an exemplarysystem a central processor can, for example, implement data miningalgorithms to operate on stored immunologic and background data to finda set of correlations. Such data mining algorithms can for example, beused to corroborate known or expected relationships, such as, forexample, a correlation in antibody levels for measles, mumps and rubellain persons born in the United States after 1960, where the threevaccines were given simultaneously. In fact, an interesting follow-upwould be to track if the rates of antibody levels for each of thesethree diseases change in the individual at a similar or a differentrate, and if different, determine why.

Alternatively, for example, such data mining algorithms can be used tofind counter-intuitive, or generally unknowns connections betweenvariables or fields in the database.

In either case, once a set of correlations is obtained, intelligence inan exemplary system can be used to automatically generate a set ofhypotheses to explain such correlations (or, if known, any follow-updata related thereto, as described above) and proceed to test theviability of each hypothesis using the data in the database. Or,alternatively, such intelligence can inform a user that additional datais needed to vet a hypothesis.

This process is explained more fully in Section II below.

Further, using such correlations, an exemplary system can, for example,also take as inputs various business rules in generating a decisionregarding a recommendation. For example, as described more fully belowin Section III, an exemplary embodiment of the present invention can beutilized to help a health insurance underwriter manage its population ofinsureds. There can, for example, be an annual or semi-annualrequirement of all insureds to have assays for various immunologicalcomponents conducted on their blood or other bodily fluids. Afteranalysis of the results of such assays, an insurance company candetermine whether a particular insured is susceptible to one or moregiven diseases or other ailments which would result in increasedexpenditures for medical treatment. The insurance company could thendecide if it was more economical to require the insured to undergocertain prophylactic treatments, such as, for example, vaccines orimmune system boosting therapies, etc., where the cost of suchprophylactic therapies is less than, as determined by some userdetermined factor, the expected exposure for medical care if the insuredcontracts one or more of the diseases or ailments to which he or she issusceptible.

In such context, there would need to be a number of business rules wheresuch user defined quantities, threshold levels, cost functions ormetrics, figures of merit, expected risks, etc., can be input andarticulated or incorporated in a number of rules. Such rules can then betaken into account by the central processor in implementing algorithmswhich take as inputs data from business rules database 220 as well as aprimary ImmunoScore database 203.

Given the generalized exemplary method of FIG. 1 and the generalizedexemplary systems of FIGS. 2, 2A and 2B, what is next described are anumber of exemplary assay panels which can be administered to anindividual or members of a population according to exemplary embodimentsof the present invention. The scientific background behind the variousexemplary assay panel, as well as which segments of the generalpopulation such panels are best administered to, are also described.

Exemplary Assay Panels

The present invention is, inter alia, concerned with assessing the“protective immune status” or “immunologic status” of an individual orpopulation. A “protective immune status” is understood to be representedby an array of detectable components (phenotypic and/or genotypic) of animmune system (adaptive and/or innate) that comprise its protectivecapacity against harmful substances and/or cells (such as, for example,microorganisms or cancer). Such components can, for example, consist ofgenes as well as gene products. Genes can include, for example, thosewhich encode immunologic receptors (such as, for example, toll-likereceptors (“TLR”s) and chemoattractant receptors) as well as effectormolecules (such as, for example, cytokines and chemokines) which mayalso, for example, exist as genetic polymorphisms capable of deleteriousand/or beneficial effects. Gene products can include, for example,antibodies, complements, cytokines, chemokines, chemoattractantreceptors, TLRs, lectins, and other immune-related ligands. Harmfulsubstances can consist of, for example, chemicals and/or toxinsoriginating from the environment, microorganisms, or one's self.

Once diagnostic information is acquired from an individual regarding hisor her immune status, this information can be, for example, added to asystem database. Such a database can contain, for example, not only theresults of ImmunoScore diagnostic testing but a wide variety ofdemographic data and patient history information as well. Such a systemdatabase can, for example, be used to record adverse events occurringcoincident with immunizations. Such information can be invaluable to,for example, the ACIP for making recommendations regarding immunizationscheduling, as well as help discover unsuspected patterns andcorrelations relevant to immune status and immune response.

ImmunoScore diagnostic testing can be, for example, tailored to meet anindividual's specific immunization status needs. In addition, eachindividual can, for example, receive their own personal ImmunoScore cardthat they could carry with them to health care office visits, and thedatabase information can be easily transferable in the ever-increasinglylikely event that they change physicians or other primary health careproviders. Additionally, ImmunoScore data, analysis of such data andrelevant database information can, for example, be stored as part of aperson's totality of health information and medical records, inelectronic formats such as, for example, entries in electronic healthinformation databases, or computer chips embedded in, for example,“smart” cards or “smart driver's licenses.”

For economy of description, most of the references cited herein areprovided in full citation in Appendix A to the Immunologic InformaticsPatent. Throughout the text citations are made to author and year ofpublication alone.

One component of ImmunoScore data can be, for example, the raw as wellas processed results of diagnostic tests or assays relating to immunestatus, as described below. ImmunoScore diagnostic testing is envisionedto be done on a small assay device or testing instrument that can belocated, for example, in a doctor's office. The testing can be done, forexample, with a sample of an individual's whole blood, plasma, serum,saliva, milk, semen, tears, or urine. In the case of blood, for example,the sample can be obtained by a finger prick, heel stick, ear stick,other skin prick, capillary draw, venous draw, or an arterial draw. Theinstrument can, for example, take assay panels and the patient sample.Patient information can also be input. The resulting information can be,for example, displayed to a user, printed, stored in a removal medium,stored in the instrument, and/or transmitted (wired or wireless) toother devices such as via an intranet, a VPN or the Internet, forexample.

Numerous systems and methods have been developed for the detection andquantitation of analytes of interest in biochemical and biologicalsubstances that can be used, for example, in such an instrument. Suchmethods and systems which are capable of measuring trace amounts ofmicroorganisms, pharmaceuticals, hormones, viruses, antibodies, nucleicacids and other proteins can be of great value to researchers andclinicians.

A substantial body of art has been developed based upon well knownbinding reactions, such as, for example, antigen-antibody reactions,nucleic acid hybridization techniques, and protein-ligand systems. Thehigh degree of specificity in many biochemical and biological bindingsystems has led to many assay methods and systems of value in researchand diagnostics. Typically, the existence of an analyte of interest isindicated by the presence or absence of an observable “label” attachedto one or more of the binding materials. Of particular interest arelabels which can be made to luminesce through photochemical, chemical,and/or electrochemical means. “Photoluminescence” is the process wherebya material is induced to luminesce when it absorbs electromagneticradiation. Fluorescence and phosphorescence are types ofphotoluminescence. “Chemiluminescent” processes entail the creation ofluminescent species by chemical transfer of energy.“Electrochemiluminescence” entails creation of luminescent specieselectrochemically.

Electrochemiluminescent (ECL) assay techniques are an improvement overchemiluminescent techniques. They can, for example, provide a sensitiveand precise measurement of the presence and concentration of an analyteof interest. In such techniques, the incubated sample is exposed to avoltammetric working electrode in order to trigger luminescence. In theproper chemical environment, such electrochemiluminescence is triggeredby a voltage impressed on the working electrode at a particular time andin a particular manner. The light produced by the label is measured andindicates the presence or quantity of the analyte. For a fullerdescription of such ECL techniques, exemplary reference is made to U.S.Pat. Nos. 5,221,605; 5,705,402; 6,140,138; 6,325,973; and 6,451,225. Thedisclosures of the aforesaid patents are hereby incorporated herein byreference.

Amplification techniques for nucleic acids may be combined with theabove assay techniques. For example, U.S. Pat. No. 6,048,687 discloseshow NASBA can be combined with an ECL technique; and U.S. Pat. No.6,174,709 discloses how PCR can be combined with an ECL technique. Thedisclosures of the aforesaid patents are also hereby incorporated hereinby reference.

An assay instrument can, for example, be, or be similar to, the BioVerisCorporation M1R or M1M instruments with an added sample processing frontend [Roche products]. Aspects of these instruments are disclosed inpending U.S. patent application Ser. Nos. 10/600,165 and 10/841,569,each under common assignment herewith. The disclosures of these patentapplications are hereby incorporated herein by reference.

In exemplary embodiments of the present invention, an assay instrumentcan include, for example, amplification techniques such as PCR or NASBA.In exemplary embodiments of the present invention, the instrument canuse fluorescence, chemiluminescence, or ECL assay techniques. Inexemplary embodiments, multiple measurements can be done simultaneously;in other exemplary embodiments of the present invention, multiplemeasurements can be done sequentially. In exemplary embodiments of thepresent invention, an assay instrument can, for example, containself-test and/or self-calibration components.

In exemplary embodiments of the present invention, a sample can be addedto an assay panel, and the combination then inserted into the testinstrument, as shown in FIG. 3. In alternate exemplary embodiments, thesample and assay panel can be separately inserted into the testinstrument, as shown, for example, in FIG. 4.

As described below, entries to an exemplary master ImmunoScore databasecan be, for example, coded so as to protect patient confidentiality. Apatient could, however, be able to learn from their physician in realtime, for example, which vaccines he or she might need to ensureprotection from vaccine-preventable illnesses. The physician can, forexample, offer the vaccine, or other therapy, during the same visit, orshortly thereafter. Any possible adverse effects from any deliveredvaccinations could be subsequently entered into an ImmunoScore databaseand that information could be shared with the ACIP or other agencies orbodies, as described more fully below.

The actual assays can be performed, for example, based upon the needs ofthe individual or individuals being examined. Age, occupation, travelplans, immigration status, military status, and previous health statuscan all be considered prior to initiation of ImmunoScore diagnosticanalyses in exemplary embodiments. In exemplary embodiments of thepresent invention, the following exemplary broad categories can, forexample, be utilized as focal points for test panels:

-   -   1. Entry to primary school.    -   2. College entry.    -   3. Age 19-49 years.    -   4. Age 50-64 years.    -   5. Age >65 years.    -   6. Health-care professionals.    -   7. Military personnel:        -   recruits and officer accessions;        -   alert forces;        -   individualized according to occupational or personal needs;            and        -   veterans.    -   8. Travelers.    -   9. Immigrants.    -   10. Individuals with identifiable health risks (not necessarily        exclusively):        -   a. Complement-deficient individuals (e.g. meningococcal            disease susceptibility);        -   b. Genetically identified (e.g. HLA haplotype, sepsis            susceptibility) disease-susceptible individuals;        -   c. Mannose-binding lectin-deficient individuals;        -   d. Hepatitis B vaccine poor/non-responders; and        -   e. Ethnic groups and others known to respond poorly to            polysaccharide, conjugate, or other vaccines.

A. College Student Diagnostic Panels 1. Meningococcal Diagnostic Panel

In exemplary embodiments of the present invention, the following testscan be included in a meningococcal diagnostic panel:

1. Antibody (Ig) to (4 tests):

-   -   Group A Meningococcal Polysaccharide (GAMP)    -   Group C Meningococcal Polysaccharide (GCMP)    -   Group Y Meningococcal Polysaccharide (GYMP)    -   Group W-135 Meningococcal Polysaccharide (GWMP)        2. Antibody (IgM) to Group B Meningococcal Polysaccharide (GBMP)        (1 test)        3. Serum levels of complement components (7 tests):    -   C5    -   C6    -   C7    -   C8    -   C9    -   Properdin    -   MBL        4. Measurement of genetic polymorphisms (5 tests):    -   FcγRIIa receptor    -   IL-1    -   IL-1R    -   IL-6    -   IL-10

2. Sexually Transmitted Diseases Assay Panel

In exemplary embodiments of the present invention, the following testscan, for example, be used for ImmunoScore measurement of immunity toSTDs:

-   -   Antibodies to Chlamydia—IgG, IgA, and IgM (3)    -   Antibodies to HSV—IgG to HSV-1 and HSV-2 (2)    -   DNA analyses of HPV types—particular emphasis on high-risk    -   Antibody to N. gonorrhoeae (1)    -   Antibody to T. pallidum (1)    -   T-cell related response to T. pallidum    -   Antibody to HIV    -   T-cell related response to HIV    -   Antibodies to GBS serotypes (at least 3)    -   Measurement of Th1/Th2 cytokines (many as current evolving        definitions)

3. Persistent Immunity Induced by Childhood Vaccines

In exemplary embodiments according to the present invention, thefollowing tests for measurement of immunity to childhood vaccines can beincluded in an exemplary ImmunoScore panel directed to college students,or in other exemplary embodiments, to adults in general:

-   -   Antibody to HBs (1)    -   Antibody to diphtheria toxin (1)    -   Antibody to tetanus toxin (1)    -   Pertusis antibodies (4):    -   Antibody to pertussis toxin (PT)    -   Antibody to pertactin (PRN)    -   Antibody to filamentous hemagglutinin (FHA)    -   Antibody to fimbriae    -   Antibody to PRP (Hib) (1)    -   Antibodies to poliovirus serotypes P1, P2, and P3 (3)    -   Antibody to measles (1)    -   Antibody to mumps (1)    -   Antibody to rubella (1)    -   Antibody to varicella (1)    -   Antibody to pneumococcal serotypes (7)

B. Adult Diagnostic Panels 1. Measurement of Immunity Induced ByVaccines for Military Personnel

In exemplary embodiments of the present invention military personnel canbe administered the following diagnostic panels:

-   1. College Student ImmunoScore Panels consisting of:    -   Meningococcal Diagnostic Panel;    -   Sexually Transmitted Disease Diagnostic Panel;    -   Persistent Immunity Induced by Childhood Vaccine Diagnostic        Panel; and        as described above; and. in addition-   2. Military personnel can have specific vaccination needs as    outlined in Table 3 below depending on their assignments and type of    deployment. Specific branches of the service may also have specific    vaccination needs and permutations of the basic diagnostic panels.    Thus, in exemplary embodiments, military personnel can be    administered one or more of the following tests:

TABLE 3 Vaccine Diagnostic Panels Exclusive to the Military: VaccineAntibody Marker Adenovirus 4 & 7 Neutralizing antibody Anthrax PACholera LPS IgG Plague Fraction I Capsular Antigen Smallpox Neutralizingantibody Lyme disease OspA

2. ImmunoScore Measurement of Vaccine-Induced Immunity for Travelers

In exemplary embodiments of the present invention, an ImmunoScoretraveler's assay panel can, for example, include the following:

-   -   Antibody to HAV (1)    -   Antibody to HBs (1)    -   Antibody to Japanese Encephalitis (1)    -   Antibody to rabies (1)        -   other rabies related cytokine assays (as necessary)    -   Antibody to Typhoid fever (1)    -   Antibody to yellow fever (1)    -   Antibody to diphtheria toxin (1)    -   Antibody to tetanus toxin (1)    -   Pertusis antibodies (4):        -   Antibody to pertussis toxin (PT)        -   Antibody to pertactin (PRN)        -   Antibody to filamentous hemagglutinin (FHA)        -   Antibody to fimbriae    -   Antibodies to poliovirus serotypes P1, P2, and P3 (3)    -   Antibody to measles (1)    -   Antibody to mumps (1)    -   Antibody to rubella (1)

3. Cytokine Measurement in ImmunoScore Introduction

An individual's immune system functions as an informational system thatis shaped during that person's life after exposure to pathogens. Immuneinterventions, such as vaccines, that manipulate the “knowledge” of theimmune system are among the most cost effective in modern medicine.Currently, globally immunotherapy for non-communicable diseases is notshowing the same success achieved in fighting infection. Despiteconsiderable experimental advances in understanding immune tolerance,autoimmune diseases continue to be treated by non-specificimmunosuppression. The substantial experimental data generated withanimal models remain limited in their capacity to allow predictions andguide clinical interventions (Lage, 2008).

The immune system should be considered a complex network, given that itconsists of more than 200 cytokines and chemokines and contains millionsof lymphocyte clones and its macroscopic activity is dictated by theinteractions of all these components. How complexity influencesimmunology is demonstrated by the almost universal failure to predictthe outcome of gene-inactivation experiments, the absence of effectivevaccines for malaria and other parasites, tuberculosis or HIV, and thecontext dependent effects of some immunotherapy interventions (whichinduce either tolerance or immunity).

Over recent decades the immune system has been subject to a great dealof investigation. Growing complexity has often been a major byproduct ofthe discoveries reported, and subsequently models such as the Th1/Th2paradigm, were developed to cope with such complexity. Regardingautoimmune diseases, verifying and expanding such models is desirable,because it has proven difficult to extrapolate findings to existingmodels that were often developed in different contexts (Delaleu, et al.2008). Recent technological advances have greatly increased the amountof information and the number of proteins that can be investigated inany given system and put into a scientific context simultaneously.

By studying the immune system through the application of reductioninstprinciples, its mediators have been thoroughly analyzed over recentdecades. This has yielded tremendous scientific advances. However,studying the properties of the immune system's isolated components islimited in terms of elucidating how system properties emerge, becausethey may strongly rely on and arise from interactions between numeroussystem components. The complexity of the immune system should notparalyze immunology research. The realization that the immune system isa complex network has led to wider use of mathematical models forsimulating its activity and testing hypotheses in silico. ImmunoScoretechnology represents a novel way to analyze the implications ofmultiple molecules in a specific condition and provide insight into theinter-relationships that define a specific immune system status.

Cytokines are a large and diverse group of plasma-membrane associated orsecreted proteins that bind cell-surface receptors and thereby regulatemany important biological processes. These processes includedevelopment, hematopoiesis, inflammation, immune responses, and tissuerepair. Whether in a healthy individual or in an acute or chronicdisease situation, cytokines act in concert rather than in isolation,and no single cytokine in a cross-sectional model is adequate to serveas an absolute screening marker. It is essential to understand theregulation of cytokine production in healthy individuals as well asindividuals with distinct disease states. The application of ImmunoScoretechnology to cytokine analyses will help to establish the viability andmerits of a multi-marker approach for clinical risk stratification.ImmunoScore technology will examine expressed levels of cytokines asserum markers, as correlation between mRNA levels and protein expressionhas previously been demonstrated to be poor in a model of autoimmunedisease (Hu, et al. 2007).

Introduction: T-Helper Cell Subsets

Uncommitted CD4+ T helper cells can be induced to differentiate towardsT helper 1 (Th1), Th2, Th17, and regulatory (Treg) phenotypes accordingto the local cytokine milieu (FIG. 1). Th1 cells secrete (among others)IFN-γ and TNF-α, which allow these cells to be particularly effective inprotecting against intracellular infections by viruses and bacteria thatgrow in macrophages, as well as eliminating cancerous cells (Kidd,2003). Th2 cells secrete IL-4, IL-5, IL-10, and IL-13 which upregulateantibody production and target parasitic organisms. Th2 cells activate Bcells, which are adapted for defense against parasites that arevulnerable to IL-4 switched IgE production, IL-5 induced eosinophilia,and IL-3 and IL-4 stimulated mast cell proliferation and degranulation(Kaiko, et al. 2007). Th17 cells secrete IL-17, IL-17F, IL-6, IL-22, andTNF-α and appear to play a role in both tissue inflammation andactivation of neutrophils to combat extracellular bacteria. Treg cellssecrete IL-10 and TGF-β, which modify helper T cell activity andsuppress some of their functions, inducing tolerance to antigens.

Anomalous T cell responses bolster a range of diseases, includingasthma, allergy, and autoimmune disease. Fundamental immune elements ofthese diseases are the development of antigen-specific T-helper cells.Th1, Th2, and Th17 cells are associated with the clinical features anddisease progression. The phenotypes of these polarized T cells thatdifferentiate from naive precursors is determined by the complexinteraction of antigen presenting cells with naive T cells and involvesmyriad factors, including the dominant cytokine environment,co-stimulatory molecules, the type and amount of antigen presented, anda wide variety of signaling cascades. The decision to take the immuneresponse in a certain direction is not made by one signal alone, butrather through many different elements acting synergistically,antagonistically, and through positive and negative feedback loops toactivate a Th1, Th2, or Th17 immune response, or combination thereof(Kaiko, et al. 2007).

Cytokines are the most influential factors that modulate T cellphenotype, and their mechanism of action involves intracellular signalstransmitted through cytokine receptors expressed on the surface of Tcells. In essence, any cell that differentially secretes or consumes keycytokines can regulate the function of other effector cells that areactivated in close proximity (Sojka, et al. 2008).

Evolution of Th1/Th2 Paradigm to Include Th17 and Treg Cells

The initial concept of the Th1/Th2 paradigm is depicted in FIG. 2, wherethe T helper cell immune response was balanced on opposite sides of ateeter-totter. Cytokines produced during one type of response wereimagined to be counter-productive to the other type of response in thismodel, expression of the Th1 response would cause a dampening of the Th2response, and vice versa. Chronic over-expression of either typeresponse would be undesirable to the individual, with a chronic Th1response seen to cause autoimmunity and graft rejection, among others,and a chronic over-expression of the Th2 response to be the cause ofatopic diseases and allergies.

With the discovery of Th17 and the re-discovery of Treg cells, it becameapparent that the teeter totter model depicted in FIG. 2 was toosimplistic, and the newer models now include the Th17 and Treg arms toaccommodate these cell types (FIG. 3). In the recent past, inflammatoryresponses were assigned as an over-expression of Th1 cells, while at thesame time allowing that inflammation could occur in the absence of thesignature cytokine of Th1 cells, IFN-γ. Now, the Th17 cell pool is seenas having a significant contribution to inflammation. Th17 responses inthe presence of a Th1 response can presumably lead to autoimmunedisease, while Th17 responses in the presence of a Th2 response can leadto allergic or atopic disease. Treg cells are envisioned as the cellsthat dampen the immune response to avoid autoimmune and allergicreactions, however, over-expression of the Treg cell population is alsonot desirable as this can lead to chronic infection, or more strikingly,acute, fatal infection.

Th1 Cytokine Signals

Th1 cell development begins with the secretion of IL-12 and type 1 IFNs(IFN-α and IFN-β). These cytokines are released by macrophages anddendritic cells (DCs) upon activation by intracellular pathogens(Farrar, et al. 2002). IL-12 induces the production of IFN-γ from theTh1 cells, which then acts in an autocrine manner to generate a positivefeedback loop, producing more IL-12. IFN-γ acts as an inhibitor of theTh2 pathway by preventing Th2 cell proliferation. Once the IL-12receptor is expressed, IL-12 is then able to bind its receptor andfurther reinforce the differentiation of Th1 cells. IL-12 signalingactivates the transcription factors STAT-3, STAT-4 and nuclear factor-κBto promote the production of cytokines associated with the Th1 phenotype(Kaiko, et al. 2007). The IFN-γ secreted by Th1 cells as they developstimulates surrounding naive Th cells to begin polarization into moreTh1 cells, in a self-renewing paracrine loop (Kidd, 2003). Otherproposed Th1 polarizing factors include IL-27, and the intercellularadhesion molecule-1 (ICAM-1) binding its receptor (Salomon andBluestone, 1998).

Th1 Effector Cell Signature:

-   -   Induced by: IL-12    -   Produce: IFN-γ, TNF-α, IL-2    -   Suppressed by: IL-10, TGF-β, IL-23

Th2 Cytokine Signals

The production of Th2 effector cells primarily involves the action ofcytokines IL-4, IL-6, IL-10, and IL-11. IL-4 induces the production ofSTAT-6 in naive T cells, which in turn activates the expression of thezinc finger transcription factor GATA-3 (Ouyang, et al. 1998). GATA-3augments promoter activity or reverses chromatin structure basedsuppression of regions that are responsible for controlling Th2 cytokinegene expression. This results in the release of cytokines characteristicof the Th2 phenotype: IL-4, IL-5, IL-9, IL-10, and IL-13. Another resultis the inhibition of expression of IL-12 receptor and therefore Th1development (Farrar, et al. 2002). As Th2 cells mature, they produceincreasing levels of IL-4, which generates a paracrine loop and inducesneighboring naive T cells to develop to Th2 cells (Kidd, 2003). IL-6 isalso released early in Th2 cell development, and up-regulates IL-4 andinhibits STAT-1 phosphorylation, thereby preventing IFN-γ synthesis(Dodge, et al. 2003). IL-6 also plays an integral role in Th17differentiation. IL-11 released by myeloid cells acts directly on Tcells to stimulate IL-4 and IL-5 synthesis and also to inhibit IFN-γproduction.

The induction of mast cell degranulation and the release of histaminehave been demonstrated to polarize the function of DCs and Th cellstowards a Th2 phenotype (Mazzoni, et al. 2006). Degranulation reducedthe capacity of DCs to induce Th1 cells and instead promoted thedevelopment of increased numbers of IL-4 secreting T cells. Thisindicates that mast cells may have a critical function in thedevelopment of the antigen specific Th2 cell phenotype in mastcell-mediated diseases, such as asthma.

It seems likely that the inducible co-stimulator (ICOS) is capable ofco-stimulating distinct effector functions, depending on the density ofsurface expression and tissue localization of the immune response. Thereappears to be a relationship between ICOS cell-surface density and thetype of cytokines produced (Kaiko, et al. 2007). There is a strongassociation between intermediate expression of ICOS and secretion of Th2cytokines, and high levels of ICOS expression and release of theregulatory cytokine IL-10 (Lohning, et al. 2003).

Th2 Effector Cell Signature:

-   -   Induced by: IL-4    -   Produce: IL-4, IL-5, IL-13, IL-10    -   Suppressed by: IL-10, TGF-β

Th17 Cytokine Signals

Th17 cells represent a subset of CD4+ cells that is both distinct fromand antagonized by cells of the Th1 and Th2 lineages. Although foundthroughout the body, Th17 cells are predominantly found in the lung anddigestive mucosa suggesting a homeostatic role in those tissues(Kryczek, et al. 2007). The generation of Th17 cells is inhibited byIL-4 and IFN-γ potentially by down-regulation of the IL-23 receptor(Harrington, et al. 2005). IL-23 appears to be essential for theproduction of a robust Th17 response, but is not responsible for theinitial induction of the Th17 phenotype. Rather, Th17 cells appear to beinduced by a combination of IL-6 and TGF-β. The combination of thesecytokines induces the predominant generation of Th17 cells with minimalnumbers of Tregs in a mutually exclusive pattern (Veldhoen, et al. 2006;Bettelli, et al. 2006). As TGF-β is involved in the development of bothTregs and Th17 cells, which may occur through the inhibition of IL-4-and IFN-γ-dependent pathways, it appears that IL-6, a known inhibitor ofTreg development, plays an integral role in switching between theseinflammatory and suppressive cell types (Kaiko, 2007). NeutralizingIL-17 in cultures of Th17 cells alters the balance in favor of Tregs,suggesting an important inhibitory action of IL-17 on Treg cells(Nardelli, et al. 2004).

Th17 Effector Cells Signature:

-   -   Induced by: TGF-β, IL-6    -   Produce: IL-17, IL-21, IL-22    -   Maintained by: IL-23    -   Suppressed by: IL-4, IFN-γ, IL-2, IFN-α

Treg Cytokine Signals

Every adaptive immune response involves recruitment and activation ofnot only effector T and B cells but also Tregs, and that the balancebetween the two populations is critical for the proper control of thequality and magnitude of adaptive immune responses and for establishingor breaching tolerance to self- and non-self antigens (Sakaguchi, et al,2008). The exact mechanism by which the Tregs exert their effect iscurrently unknown, although it is believed that their suppressivefunction may be contact-dependent (Afzali, et al. 2007). Other studiesshow an important role for TGF-β and IL-10 production as mediators ofTreg activity that is contact-independent (Dieckmann, et al. 2002;Longhi, et al. 2006). Both TGF-β and IL-2 are important for thedevelopment of Tregs (Afzali, et al. 2007; Malek and Bayer, 2004). Foxp3expression as a complex leads to Treg cell-mediated suppression in acell-cell contact-dependent or -independent manner (Li and Greene,2008). In humans, disruption of Foxp3 function leads to an immunedysfunction, polyendocrinopathy, enteropathy, X-linked (IPEX) syndromecharacterized by autoimmune disease, allergy, and inflammatory boweldisease (Bennett, et al. 2001). In the absence of cell-cell contact,Tregs can suppress T cell activity by either directly secreting IL-10,TGF-β, and IL-35, or competing for cytokines via receptors that containthe common γ-chain, which binds to IL-2, IL-4, and IL-7 (Sojka, et al.2008).

Given the diverse array of suppressive mechanisms, Treg activity needsto be attenuated to mount effective immune responses to infection. Tregfunction can be modulated by a variety of pro-inflammatory signalsincluding Toll-like receptor triggering and direct inhibition by tumornecrosis factor-α (TNF-α) (Liu and Zhao, 2007; Valencia, et al. 2006).The up-regulation of B7 expression (CD80 and CD86) by antigen-presentingcells represents a central event in the activation of naive T cells andmay serve as a mechanism to disrupt regulatory T cell tolerance byrendering effector T cells unresponsive to suppression (Sojka, et al.2008). B cells with their lower expression of CD80/CD86 appear to bemore efficient antigen-presenting cells than dendritic cells forinducing effector Treg cells (Benson, et al. 2007).

Treg Cell Signature:

-   -   Induced by: TGF-β, IL-2    -   Produce: IL-10, TGF-β, IL-35    -   Suppressed by: IL-6, IL-17, IL-31

Dendritic Cells

Dendritic cells are recognized as one of the most important cell typesfor initiating the priming of naive CD4+ helper T (Th) cells and forinducing CD8+ cell differentiation into killer cells (Banchereau, et al.2000). Immature dendritic cells are found at strategic anatomical sitesthroughout the body, thereby allowing them to respond rapidly tomicrobial invasion (Pashine, et al. 2005). Activation of lymphoiddendritic cells, because of their preponderance to secrete IL-12, may beimportant for priming Th1-like responses, while early activation ofmyeloid dendritic cells may lead to Th2-like responses (Pulendran,2004). It has also been suggested that the production of Il-6 bydendritic cells may be responsible for inhibiting the suppressoractivity of Treg cells (Pasare and Medzhitov, 2004). This production ofIL-6 might also reasonably be assumed to enable the activation of Th17cells.

B Cells and Success of Vaccinations

The most dramatic health problem of the aged immune system is theincreasing rates of morbidity and mortality from recurrent and invasiveinfections of the respiratory tract caused by encapsulated bacteria suchas Streptococcus pneumoniae (Sankilampi, et al. 1997). It has beenreported that increased susceptibility to secondary pneumococcalpneumonia is at least in part caused by excessive Il-10 production andreduced neutrophil function in the lung (van der Sluijs, et al. 2004).Two populations of B cells have been identified in human peripheralblood: mature and memory B cells. IgD-CD27+ memory B cells can produceIgG, IgM, and IgA, while IgD+CD27+ IgM memory B cells predominantlyproduce IgM (Shi, et al. 2003). The presence of IgM memory B cells inthe blood correlates with protection from pnuemococcal infection(Kruetzmann, et al. 2003). Natural antibodies make up most of the IgM inthe serum and have the function to limit the growth and dissemination ofpathogens during the early phases of infection and potentiate the immuneresponse (Ochsenbein, et al. 1999). Physiological and transientdisposition to pneumococcal infection of young children (under 2 yearsof age) is associated with the lack of circulating IgM memory cells andof serum anti-polysaccharide IgM (Kruetzmann, et al. 2003). Decline ofsplenic functions may reflect diminished numbers of aged IgM memory Bcells. Effectiveness of pneumococcal polysaccharide vaccine in olderadults on protection against pneumococcal infections may be associatedwith the increase and activation of circulating IgM memory B cells,resulting in rapid synthesis of anti-polysaccharide IgM antibodies (Shi,et al. 2005).

Regulatory B Cells

The maintenance of tolerance is an absolute requirement of asophisticated regulatory apparatus to prevent or dampen overzealousimmune responses. In addition to the ability of B cells to prime andactivate the immune system, B cells with regulatory function (Bregs)have been identified in experimental models of autoimmunity, infections,and cancer, supporting the notion that, similar to Tregs, Breg-mediatedsuppression is an important means for the maintenance of peripheraltolerance. This regulatory function appears to be directly mediated bythe production of IL-10 and TGF-β and by the ability of the B cells tointeract with pathogenic T cells to inhibit harmful immune responses(Mauri and Ehrenstein, 2008). B cells are typically characterized bytheir ability to produce antibodies. However, B cells possess additionalimmune functions, including the production of cytokines and the abilityto function as secondary antigen presenting cells. As with T cells, theB cell population contains functionally distinct subsets capable ofperforming both pathogenic and regulatory functions (Mizoguchi and Bhan,2006). B cells can play a pathogenic role in acquired immune responsesby producing autoantibodies that contribute to the development ofautoimmune diseases (Murakami and Honjo, 1997; Korganow, et al. 1999;Fields, et al. 2003). The existence of an immunoregulatory B cell subsetthat plays a role in immune regulation resulting in complete recoveryfrom experimental autoimmune encephalomyelitis was reported in a murinemodel of that disease (Wolf, et al. 1996).

IL-10 from regulatory B cells can repress the production of IL-6 andIL-12 by DCs, thereby inhibiting the differentiation of Th17 and Th1cells, respectively (Lampropoulou, et al. 2008).

Like their T cell counterparts, B cells can be divided into functionallydistinct regulatory subsets capable of inhibiting inflammatory responsesand inducing immune tolerance (Mizoguchi and Bhan, 2006).

Role of Basophils

Basophils activated by IL-3 or antibody to FcεRI induce B cellproliferation and the production of IgM and IgG1 in the presence ofactivated CD4+ T cells; this B cell proliferation and immunoglobulinproduction requires IL-6, IL-4 and cell contact (Kawakami, 2008).Activated basophils enhance the humoral memory response by secretingIL-6 and by altering the phenotype of CD4+ cells (that is, by inducingCD4+ T cell up-regulation of IL-4, IL-5, IL-10, IL-13, and thetranscription factor GATA-3 and down-regulation of IFN-γ and IL-2)(Denzel, et al. 2008).

Trauma and Cytokines

The immune system undergoes numerous changes after traumatic injuries,including a down-regulation of the Th1 response and up-regulation of theTh2 response (Miller, et al. 2007). They Th1 response is suppressed asillustrated by diminished IL-2, IFN-γ, and IL-12 levels after majorinjury. The enhancement of the Th2 profile is marked by elevated IL-10and IL-4. Certain cytokine profiles, ratios, and polymorphisms may helpidentify patients at increased risk of systemic inflammatory responsesyndrome (SIRS), sepsis, multiple organ failure (MOF), and deep venousthrombosis. Some provocative indications for individuals moresusceptible to complications include (Miller, et al. 2007):

-   -   decreased IL-12    -   increased IL-10    -   increased sIL-2Ra    -   increased IL-18    -   IL-18 promoter genetic polymorphisms    -   IL-6:IL-10 ratio

Identification of those Th1/Th2 cytokine profiles associated with worseprognosis may one day allow clinicians to risk stratify injured patientsand identify those at increased risk of developing SIRS, sepsis, MOF,and deep venous thrombosis.

Stress and Cytokines

Recent evidence indicates that the major stress hormones,glucocorticoids and catecholamines, systemically inhibit IL-12, TNF-α,and IFN-γ, while simultaneously upregulating IL-10, IL-4, and TGF-βproduction indicating a generic Th1 to Th2 shift (Calcagni and Elenkov,2006). However, in certain local responses and under certain conditions,stress hormones may actually facilitate inflammation through inductionof IL-1, IL-6, IL-8, IL-18, TNF-α, and CRP production. Autoimmunity,chronic infections, major depression, and atherosclerosis arecharacterized by a dysregulation of the pro/anti-inflammatory andTh1/Th2 cytokine balance (Calcagni and Elenkov, 2006). These authorsstated that conditions that are associated with significant changes instress system activity, such as acute or chronic stress, cessation ofchronic stress, pregnancy and the postpartum period, or rheumatoidarthritis through modulation of the systemic or localpro-anti-inflammatory and Th1/Th2 cytokine balance, may suppress orpotentiate disease activity and/or progression. Stress-hormones inducedinhibition or up-regulation of innate and Th cytokine production mayrepresent an important mechanism by which stress affects diseasesusceptibility, activity, and outcome of various immune-relateddiseases.

Inflammatory Bowel Disease (IBD) and Cytokines

Traditional dogma has had different viewpoints of inflammatory boweldisease and cytokines: Crohn's disease (CD) has been thought to have aTh1 motif, while ulcerative colitis (UC) was thought to have given riseto a Th2 expression (Mudter and Neurath, 2007). CD has been associatedwith elevated expression of IFN-γ, TNF-α, and IL-12. In UC, the patternis less clear; there is a modified Th2 response associated withcytokines such as IL-15 and IL-10 (Torres and Rios, 2008). Otherpublications have reported that the IL-17/IL-23 pathway may have apivotal role in intestinal inflammation (Hue, et al. 2006; Kullberg, etal. 2006).

Atherosclerosis

Atherosclerosis historically was considered to be mainly a degenerativedisease, but it is now well ascertained that its pathogenesis isinflammatory (Jawien, 2008). Serum levels of the IL-1 family ofcytokines (including IL-18 and IL-33) have been correlated with variousaspects of cardiovascular disease and their outcomes (Apostolakis, etal. 2008). IL-1Ra, a natural antagonist of IL-1, possessesanti-inflammatory properties, mainly through the endogenous inhibitionof IL-1 signaling (Apostalakis, et al. 2008).

Oxidized low density lipoprotein (OxLDL) is not only pro-inflammatoryand pro-atherogenic, but several of the neoepitopes generated duringoxidation are highly immunogenic and result in the generation ofauto-antibodies. The overall evidence supports the notion that IgGauto-antibodies to OxLDL are associated with pro-atherogenic properties,and that IgM auto-antibodies to OxLDL are associated withatheroprotective properties (Gounopoulos, et al. 2007). ImmunoScorewould track trends in anti-OxLDL antibody levels in patients over timeand add this information to the ImmunoScore database. Similarly,antibody levels to Hsp 60 would be tracked to determine if theseantibodies are beneficial or detrimental to patients.

One researcher proposed that in general, it is believed that the Th1response and its mediators: IFN-γ, TNF-α, IL-1, IL-12, and IL-18 enhanceatherogenesis, while a Th2 response and its mediators: IL-4, IL-5, IL-10and IL-13 inhibit the development of atherosclerosis (Jawien, 2008).Another group put the atherosclerosis profile onto the Th17/Treg axis bystating that acute coronary syndrome was associated with an increase inTh17 cytokines (IL-17, IL-6, and IL-23) and a decrease in Treg cytokines(TGF-β and IL-10) (Cheng, et al. 2008). By measuring all the associatedcytokines over time and various demographics, the ImmunoScore technologywould be able to specifically enumerate and assign significance to theseassays and relate the results to the individuals being tested.

Another study found that in patients with an inflammatory response, asdemonstrated by elevated levels of IL-6 in serum, CMV seropositivity wasa strong and independent predictor for cardiac death (Blankenberg, etal. 2001). High CMV antibody titers may be associated with a chronicinflammatory response resulting in increased IL-6 levels. This in turn,can lead to an increase in CRP levels and a poor prognosis for coronaryartery disease outcome. It has been shown that statins can reduce theinflammatory response. Periodic ImmunoScore measurements in a treatedpatient population would assist physicians regarding course andeffectiveness of statin treatments, as serum CMV positivity withoutinflammatory response is not indicative of fatal cardiovascular events(Blankenberg, et al. 2001).

Another group proposed classifying cytokines related to bothatherosclerosis and diabetes in the following categories: “noxious”comprising IL-1, IL-2, IL-6, IL-7, IL-8, IL-15, IL-17, and IL-18;“protective” comprising IL-4, IL-10, IL-11, IL-12, and IL-13; and“aloof” comprising IL-5, IL-9, IL-14, IL-16, and IL-19 through IL-29(Fisman, et al. 2008).

Proposed ImmunoScore Atherosclerotic Disease Panel

Indicators for Poor Prognosis (Th1/Th17 Axis)

-   -   IL-6    -   IL-12    -   IL-18    -   IL-33    -   IFN-γ    -   TNF-α    -   CRP    -   Antibody to CMV

Indicators for Improved Prognosis (Th2/Treg Axis)

-   -   IL-10 (with exception of transplant patients)    -   TGF-β    -   IL-5

Indeterminate Prognostic Value (Th2)

-   -   IL-4    -   Antibody to OxLDL    -   Antibody to Hsp 60

Th17 Autoimmune Pathogenesis

Th17 may play an essential role against certain extracellular pathogens.However, Th17 cells with specificity for self-antigens are highlypathogenic and lead to the development of inflammation and severeautoimmunity. Interleukin (IL)-17 was originally named cytotoxic Tlymphocyte-associated antigen-8 (CTLA-8) (Paradowska, et al. 2007).There are six members of the IL-17 protein family (IL-17A throughIL-17F). The IL-17 family plays a key role in the regulation of immuneand inflammatory response, in the homeostasis of several tissues, andthe progression of autoimmune diseases (Paradowska, et al. 2007).

IL-23 and IL-17 are associated with a number of human autoimmunedisorders. Th17 cells are likely to be highly pathogenic in rheumatoidarthritis (McGeachy and Cua, 2008). IL-17+, CD4+ and CD8+ T cells havebeen identified in active lesions in the brain of multiple sclerosispatients (Tzartos, et al. 2008). Psoriasis has also been linked toinappropriate Th17 cell responses. IL-17, IL-23, and Il-22 are allelevated in psoriatic lesional skin (Lee, et al. 2004; Wilson, et al.2007; Wolk, et al. 2004).

As the Th1 and Th2 cell subsets cross-regulate the differentiation ofthe other cell type, they also appear to negatively regulate thedifferentiation of Th17 cells (McGeach and Cua, 2008). Addition of IL-2,IFN-γ, or IL-4 to cultures inhibits either IL23- or TGF-β plusIL-6-stimulated differentiation of mouse and human Th17 cells(Annunziato, et al. 2007; Harrington, et al. 2005; Murphy et al. 2003;Park, et al. 2005; Wilson, et al. 2007).

Foxp3+ regulatory T cells (Treg) are necessary and sufficient to preventautoimmunity throughout the lifespan of an individual. TGF-β inducesFoxp3 in naïve T cells, but TGF-β and IL-6 together drive the generationof Th17 cells (Korn, et al. 2007). A group found that in humans, IL-23and IL-1 are able to drive naive CD4+ T cells toward the Th17 phenotype(Wilson, et al. 2007).

Th17 cells probably have a specific role in normal immune functionthrough the coordinated action of their effector cytokines andchemokines, similar to the well established functions of Th1 and Th2cells in regulating cellular immunity and antibody production. Thesignature cytokine and chemokine profile of Th17 cells suggests thatthese cells regulate the immune function of epithelial cells rather thancells of the classical immune system (Wilson, et al. 2007).

When mucosal immunity is not countered by anti-inflammatory mediators,excessive pro-inflammatory responses result in chronic inflammatorybowel disease (IBD) (Braegger, 1994). One study demonstrated that IL-23was essential for the manifestation of chronic intestinal inflammation,whereas IL-12 was not. A critical target of IL-23 was shown to be aunique subset of tissue-homing memory T cells, that were specificallyactivated by IL-23 to produce IL-17 and IL-6. They concluded that thispathway might be responsible for chronic intestinal inflammation as wellas other chronic autoimmune inflammatory diseases (Yen, et al. 2006).

-   -   Th17 differentiation        -   TGF-β (may inhibit in humans?—Chen and O'Shea, 2008)        -   IL-1β (Toh and Miossec, 2007; Chen and O'Shea, 2008)        -   IL-6        -   IL-23    -   Th17 amplification        -   IL-21 (produced by Th17 cells)    -   Th17 stabilization        -   IL-23

Thymus-produced self-reactive T cells, which become activated in theperiphery by recognition of major histocompatibilitycomplex/self-peptide complexes, stimulate antigen presenting cells(APCs) to secrete IL-6. APC-derived IL-6, together with T cell-derivedIL-6, drives naïve self-reactive T cells to differentiate intoarthritogenic Th17 cells. In mice, deficiency of either IL-17 or IL-6completely inhibits the development of arthritis, while IFN-γ deficiencyexacerbates the development of arthritis (Hirota, et al. 2007). Inhumans, it is not yet clear whether rheumatoid arthritis is a Th1 or aTh17 mediated disease (Lubberts, 2008).

The IL-17 family of cytokines has been implicated in the pathogenesis ofrheumatoid arthritis (RA) and juvenile idiopathic arthritis (JIA)(Nistala, et al. 2008). In JIA, IL-17 is increased in patients withactive disease as compared to those in remission (de Jager, et al.2007). Conversely, Treg cells are present at significantly highernumbers in patients with a milder clinical phenotype than in those witha more severe form of arthritis (De Kleer, et al. 2004).

-   -   Th17 cell cytokine production (Lubberts, 2008):        -   IL-17A        -   IL-17F        -   IL-6        -   TNF-α        -   GM-CSF        -   IL-21        -   IL-22        -   IL-26

IL-6

The pleiotropic cytokine IL-6, previously called B cell stimulatoryfactor-2 (Bcl-2 or BSF-2) or IFN-β2, has emerged in recent years as akey regulator of the transition from innate to adaptive immunity throughits ability to modulate leukocyte recruitment at inflammatory sites(Kishimoto, 2006; Ohsugi, 2007). It has been found that there is athermally-sensitive alert system utilizing IL-6 signaling that promotesimmune surveillance, thus shedding light on the benefits of mounting afebrile reaction during inflammation (Vardam, et al. 2007).

One of the most important systemic actions of IL-6 is induction of theacute phase response. Acute phase proteins are produced primarily by theliver and include proteins that promote the immune response throughactivation of complement, induction of pro-inflammatory cytokines, andstimulation of neutrophil chemotaxis (Cronstein, 2007). In humans, towof the most prominent acute phase proteins are CRP and serum amyloid A(Van Snick, 1990).

IL-6 exerts a significant influence on the course of inflammation inhumans. There is evidence that IL-6 is capable of mediating bothpro-inflammatory effects, including the induction of intercellularadhesion molecules and the recruitment of leukocytes, andanti-inflammatory effects, such as the suppression of thepro-inflammatory cytokines, TNF-α and IL-1 (Wong, et al. 2003).

IL-6 in combination with its soluble receptor, sIL-6Rα, influences thetransition from acute to chronic inflammation (Gabay, 2006). Prospectivestudies have shown that long-term IL-6 levels are associated withcoronary heart disease (CHD) risk as strongly as are some majorestablished risk factors (Danesh, et al. 2008). This group pointed outthe pressing need for paired studies of individuals, in that owing tofluctuations in IL-6 values over time, comparisons using only baselinevalues may yield biased estimates of the true association between Il-6and CHD, which can be corrected, for the most part, by using data frompaired measurements. In addition, given the central role of IL-6 levelsin inflammatory pathways and its continuous association with CHD risk,it warrants further investigation as a plausible potential therapeutictarget. The ImmunoScore database will serve to hold an individual'spaired measurements of various cytokines and enable future prospectivestudies for many diseases, as well as enable the study of CHD. Inaddition, ImmunoScore diagnostic applications could track fundamentalimmune parameters in individuals undergoing IL-6 directed therapy.

Cardiovascular disease is a leading cause of mortality in rheumatoidarthritis (RA). Endothelial dysfunction often precedes manifestatherosclerosis. Among immunological and metabolic laboratory markers,anticyclic citrullinated peptide antibodies, IgM rheumatoid factor,circulating immune complexes, pro-inflammatory cytokines including TNF-αand IL-6, Th0/Th1 cells, homocysteine, dyslipidemia, decreased folateand vitamin B production, and paraoxonase activity may all be involvedin the development of vascular disease in RA (Szekanecz, et al. 2007).The early diagnosis of endothelial dysfunction and atherosclerosis,active immunosuppressive treatment, the use of drugs that controlatherosclerosis, changes in sedentary lifestyle, and the close follow-upof RA patients may help to minimize cardiovascular risk in theseindividuals.

High serum levels of IL-6 have been linked to risks for severalconditions, such as cardiovascular disease, type 2 diabetes, mentalhealth complications, and some cancers. Stress-induced immunedysregulation has been shown to be significant enough to result inhealth consequences, including reducing the immune response to vaccines,slowing wound healing, reactivating latent viruses, and enhancing therisk for more severe infectious disease. There is evidence thatpsychological stress promotes immune dysfunction that negatively impactshuman health (Godbout and Glaser, 2006).

Local cellular environmental factors and an individual's geneticsusceptibility play a role in the transduction of IL-6 signals.Depending on the expression of CD45 on multiple myeloma cells, IL-6 caneither result in proliferation or apoptosis of CD45+ cells depending oncircumstantial stimuli (Ishikawa, et al. 2006). Chronic obstructivepulmonary disease (COPD) is a multicomponent disease characterized byabnormal inflammatory response of the lungs to noxious particles,accompanied by systemic effects like weight loss, muscle wasting,reduced functional capacity, and impaired health status. A persistentlow-grade systemic inflammatory response, determined in part by geneticcomponents, is present in a portion of the COPD population (Yanbaeva, etal. 2006).

IL-22

Elevated serum and plasma levels of IL-22 are indicative of Crohn'sdisease. Normal population mean level are approximately 2 pg/mL, whilemean levels in Crohn's disease patients reach 24 pg/mL, and are higherwith flares of the disease (Wolk, et al. 2007).

IL-23

Interleukin-23 is composed of the IL-12p40 subunit and a novel p19subunit. It can enhance the proliferation of memory T cells and theproduction of IFN-γ, IL-12, and TNF-α from activated T cells. IL-23 canalso act directly on dendritic cells and possesses potent anti-tumor andanti-metastatic activity in murine models of cancer (Hao and Shan,2006).

IL-23 possesses unique roles in the differentiation and expansion ofmemory T cells. IL-23 is also associated with Th17 responses and thecytokine produced by the antigen presenting cells (i.e. IL-12 vs. IL-23)determines in part if a response is Th1 or Th17 (Tan, et al. 2008).

IL-23 is an inflammatory cytokine that plays a key role in thepathogenesis of several autoimmune and inflammatory diseases. Itorchestrates innate and T cell mediated inflammatory pathways and canpromote Th17 cell responses (Izcue, et al. 2008). IL-23 has beenassociated with several inflammatory disease including rheumatoidarthritis, inflammatory bowel disease, and Helicobacter pyloriassociated gastritis. The immune response in the intestine is typicallya delicate balance between effector and regulatory T cell responses, andIL-23 plays a key role in this balance. Factors may promote inflammationnot only by direct effects on inflammatory mediators, but alsoindirectly by impeding regulatory mechanisms (Izcue, et al. 2008). IL-6has been identified as an inflammatory mediator that desensitizes Tcells to Treg cell mediated suppression (Pasare and Medzhitov, 2003).IL-23, via its ability to impede Treg cell responses in the intestine,may promote host protective immunity at this site.

Cytokines and Autoimmunity

The presence of IL-17 mRNA or IL-17 protein in tissues and biologicalfluids of patients has been associated with rheumatoid arthritis(Kotake, et al. 1999; Honorati, et al. 2001), multiple sclerosis(Matusevicius, et al. 1999; Kurasawa, et al. 2000), systemic lupuserythematosus (Wong, et al. 2000), inflammatory bowel disease (Nielsen,et al. 2003; Fujino, et al. 2003), atopic dermatitis (Koga, et al.2008), Lyme arthritis (Infante-Duarte, et al. 2000), and psoriasis(Albanesi, et al. 2000).

In autoimmunity, IFN-γ does not appear to be pathogenic, but ratherprotective, as inhibition of IFN-γ signaling enhances the development ofpathogenic Th17 and exacerbates autoimmunity (Harrington, et al. 2005).Also, the neutralization of IL-4, produced by Th2 cells is critical toin neutralizing the development of IL-17; however, neither IFN-γ norIL-4 seem to be effective on already established Th17 pathogenesis(Harrington, et al. 2005).

In organ-specific autoimmunity, the balance of cytokines is a keydeterminant of resistance or susceptibility. Animal models ofexperimental autoimmune encephalomyelitis (EAE) are considered to mirrorconditions of multiple sclerosis (MS) in humans. In EAE, diseasesusceptibility is thought to correlate with the expression ofpro-inflammatory cytokines such as IL-17, IFN-γ, TNF-α, IL-6, and IL-1β.On the other hand, Th2 cytokines such as IL-4 and IL-13 have been shownto be important for preventing or easing disease symptoms (Cash, et al.1994; Olsson, 1995). IL-25 is expressed in organ systems whereregulation of inflammation is of critical importance (Kleinschek, et al.2007). In healthy digestive and respiratory tracts, an anti-inflammatoryenvironment must be maintained due to constant exposure to commensalmicrobes.

Although IL-25 and IL-17 are members of the same cytokine family, theyplay opposing roles in the regulation of organ-specific autoimmunity.The type 2 responses promoted by IL-25 drive a novel regulatorymechanism for controlling Th17 responses. This regulation relies onIL-13 and not IL-4, suggesting that IL-13 may be secreted at higherlevels in the target organs during autoimmune inflammation (Kleinschek,et al. 2007).

In mice, IL-25 is expressed by lung epithelial cells as a result ofinnate immune responses to allergens. IL-25 promotes Th2 celldifferentiation in an IL-4-dependent manner and has been shown to be acritical factor regulating the initiation of innate and adaptivepro-allergic responses (Angkasekwinai, et al. 2007).

Human patients with IBD have elevated IL-17 and IL-22 in affectedcolonic tissue and serum, depending on disease activity and severity(Fujino, et al. 2003; Nielsen, et al. 2003; te Velde, et al. 2007).Patients with rheumatoid arthritis have elevated Il-17 and IL-22 insynovial fluid (Kotake, et al. 1999; Ikeuchi, et al. 2005). Il-22 isincreased in psoriatric serum and high levels of IL-23 have beendetected in psoriasis lesions (Wolk, et al. 2006; Piskin, et al. 2006).

Etanercept is a TNFR-Ig fusion protein that has been used clinically toblock TNF at molecular and cellular levels. A group studying the effectof this drug on psoriasis patients found that improvement in psoriasisdisease correlated with the rapid down-modulation of DC and Th17 cellproducts and downstream effector molecules. Final disease resolutioncorrelated with later down-modulation of Th1 cells (Zaba, et al. 2007).

Although many patients have been treated with immunomodulatory drugs,there are surprisingly limited data on therapeutic mechanisms in humaninflammatory disease. Consistent monitoring of serum cytokines duringtherapies for autoimmune disease with ImmunoScore technology willprovide benefit to physicians, patients, and pharmaceutical researchers.In the case of etanercept treatment of psoriasis described above, it wasfound that first DC and Th17 effects in lesions were lessened, butdisease was not completely resolved until Th1 effects were alsoameliorated. For other inhibitors of TNF (of which there are threecurrently in clinical use), there may be different mechanisms as yetundiscovered. Careful monitoring of the progress of autoimmune diseasesand treatments to ease disease symptoms are to be a hallmark ofImmunoScore implementation. Based upon current understanding ofautoimmune disease flares, the following cytokines are to be monitoredin patients suffering from autoimmune disease symptoms. Progressivespikes of each of these cytokine groupings are to be expected. Effectivetherapeutic treatment should be indicated by faster cycling of eachstage.

-   -   1. When expressed together, these cytokines indicate a “highly        aggressive” Th17 profile:        -   IL-17        -   IL-22    -   2. For Th17 induction and stability:        -   IL-23        -   IL-6        -   TGF-β    -   3. Cytokines indicative of impending flare resolution:        -   IFN-γ (Th1 cytokine—interferes with long term Th17 cell            survival)        -   IL-4 (Th2 cytokine—interferes with long term Th17 survival)        -   IL-27    -   4. Treg cytokines:        -   IL-10        -   TGF-β

It has been proposed that there is a reciprocal relationship betweenpathogenic Th17 cells and Foxp3+ Treg cells, in which IL-6, an acutephase protein induced during inflammation, acts as a pivot to determinewhether the immune response is dominated by the highly inflammatory Th17cells or protective Treg cells (Bettelli, et al. 2007).

Cytokines and Cancer

Immunosuppressive networks mediated by IL-10 and TGF-β seem to inhibitcell-mediated immune responses against cancer cells (Zou, 2005).Clinical data show a decreased ratio of circulating Th1 cells tocirculating Th2 cells and their associated cytokines in different cancertypes and also in chronic inflammatory conditions that are associatedwith increased risk of cancer (Tan and Coussens, 2007).

Increased levels of circulating cytokines and their receptors (mostoften of the pro-inflammatory cytokine IL-6) have been found inobservational studies of patients with various types of cancer, both atthe diagnosis of the primary disease and in those with metastases,compared with healthy people and people with benign tumors (Seruga, etal. 2008)

Various specific cancer treatments can stimulate the immune system toproduce pro-inflammatory cytokines that are associated with toxiceffects of treatment such as cancer-related fatigue, flu-like systemiceffects and bone loss. They can lead to impaired quality of life ofpatients with cancer and poor compliance with treatments (Seruga, et al.2008). Stimulation of the immune system by specific cancer treatmentsmight also have a substantial role in producing anti-cancer effects.Cancer drugs might differentially effect the secretion of cytokines inhumans with cancer, and this secretion might be a tool with which tomonitor the therapeutic indices of drugs. Periodic ImmunoScorediagnostic measurements of cytokines relevant to specific cancer typeswould be invaluable to doctors and their patients.

Cancer patients frequently suffer from fatigue and some suffer fromcognitive impairment during and after treatment for cancer (Lawrence, etal. 2004; Vardy and Tannock, 2007). An animal study has indicated thelikely importance of IL-6 in the development of cognitive impairment(Sparkman, et al. 2006). IL-10 has been shown to counteract theproduction of IL-6 in microglial cells (Heyen, et al. 2000). In anotherstudy, patients with myeloid leukemia or myelodysplastic syndrome whohad higher serum IL-6 levels were found to have poorer executivefunction, whereas higher levels of IL-8 were associated with bettermemory (Myers, et al. 2005).

Many cancer patients, during both treatment and long-term follow-up,experience psychological distress including anxiety and depression(Zabora, et al. 2001). Studies of chronic and acute stress showedincreased circulating levels of Il-6 and TNF-α compared with controls(Kuecolt-Glaser, et al. 2003; Graham, et al. 2006). Cancer-relatedfatigue is strongly associated with a depressed mood (Bower, et al.2006). Clinical studies have also shown an association betweencirculating levels of IL-6 and resistance to chemotherapy (De Vita, etal. 1998; Zhang and Adachi, 1999). Il-6 is one of the most ubiquitouslyderegulated cytokines in cancer patients and high levels of circulatingIL-6 most commonly predicted poor outcome in observational studies(Hong, et al. 2007). Stage is an important prognostic factor in everycancer type and in observational studies there is a consistent trend ofhigher levels of circulating cytokines in more advanced stages ofvarious cancers than in early stages, which further supports anassociation with the outcome of cancer (Seruga, et al. 2008). A study oforal squamous cell carcinoma found significant contributions of IL-6 andTNF-α to disease (Vairaktaris, et al. 2008).

Some immunomodulatory cytokines have been demonstrated to haveanti-tumor activity. These include TNF-α, IFN-γ, IFN-α, IL-2, IL-12,IL-15, and IL-18. Others with promise of anti-tumor activity includeIL-21, IL-23, and IL-27 (Weiss, et al. 2007).

Pro-inflammatory cytokines are involved in the development andprogression of cancer and are also associated with fatigue, depression,cognitive impairment, anorexia, and pain, which all affect the qualityof life of the patient. Sustained production of some cytokines may alsobe associated with cancer recurrence and progression. Strategies tomonitor and inhibit the effects of such cytokines might therefore haveprofound effects on quality of life and survival.

Proposed ImmunoScore Cancer Cytokine Panel

-   -   IL1-RA    -   sIL2R    -   IL-6    -   IL-8    -   IL-10    -   TNF-α    -   M-CSF    -   VEGF

Treg Manipulation

Depletion of naturally arising Tregs not only elicits autoimmunity, butalso augments immune responses to non-self antigens (Sakaguchi, et al.2008). Treg depletion produces inflammatory bowel disease, which likelyresults from excessive immune responses to commensal bacteria in theintestine (Singh, et al. 2001). Removal or reduction of CD4+CD25+ Tregsalso provokes effective tumor immunity in otherwise non-respondinganimals and augments microbial immunity in chronic infection, leading toeradication of tumors or microbes (Wang and Wang, 2007; Belkaid andRouse, 2005). Conversely, CD4+CD25+ T cells enriched from normal micesuppress allergy, establish tolerance to organ grafts, prevent graftversus host disease after bone marrow transplantation, and promotefetal-maternal tolerance (Sakaguchi, 2005).

Several key concepts have been formulated regarding dominantself-tolerance and immune regulation (Sakaguchi, et al. 2008). First,the normal immune system generates Tregs that are engaged in suppressingimmune responses towards self, quasi-self (such as autologous tumorcells), and non-self (such as microbes and allografts). Second, thenormal thymus produces potentially pathogenic self-reactive T cells aswell as functionally mature Tregs; mature Tregs persist in the peripheryand exert dominant control over the self-reactive T cells. Third, Tregdeficiency in the periphery is sufficient to evoke chronic Tcell-mediated autoimmunity and immunopathology.

In vitro, Tregs suppress the proliferation and cytokine production(particularly IL-2) of responder T cells in the presence of antigenpresenting cells (Takahashi, et al. 2000). Several mechanisms of Tregmediated suppression have been proposed, and these include secretion bythe Treg of immunosuppressive cytokines, cell contact dependentsuppression, and functional modification or killing of antigenpresenting cells. IL-10 and TGF-β contribute to suppression ofinflammatory bowel disease by Treg depletion (Read, et al. 2000). TGF-βmay act as a direct mediator of suppression, and/or maintain Foxp3expression and suppressive activity in Tregs (von Boehmer, 2005). Arecent study has shown that Foxp3 natural Tregs predominantly produceimmunosuppressive IL-35: ectopic expression of IL-35 confers regulatoryactivity on naive T cells, whereas recombinant IL-35 suppresses T cellproliferation (Collison, et al. 2007).

Foxp3 Tregs are abundant in tumors. Natural Tregs that promote selftolerance may act to impede immune surveillance against cancers innormal individuals and suppress potential responsiveness to autologoustumors in cancer patients. Targeting Tregs is a promising approach forcancer immunotherapy. Such approaches could include local depletion ofTregs in the tumor mass, attenuation of Treg function at the time oftherapeutic vaccination with tumor antigen, and ex vivo expansion oftumor infiltrating lymphocytes after the depletion of Tregs (Sakaguchi,et al. 2008).

Role(s) of TGF-β

TGF-β is well known as an important cytokine that promotes thedifferentiation of anti-inflammatory Foxp3+ Treg cells. The finding thatTGF-β is also required for the differentiation of pro-inflammatory Th17cells was unexpected (Veldhoen, et al. 2006; Bettelli, et al. 2006). Amodel has been proposed wherein Th17 and Treg cell subsets may worktogether to either elicit or restrain tissue inflammation (Cua andKastelein, 2006). The gut mucosa has high concentrations of TGF-β, whichmay induce a local population of Foxp3+ Treg cells to maintainhomeostasis in an environment filled with commensal bacteria. The gutlamia propria also naturally contains a considerable number of IL-17producing cells that may help to maintain the mucosal barrier (Mangan,et al. 2006). When there is a breach of the protective mucosa, dendriticcells secrete copious amounts of IL-6 and IL-23. This then, likelyactivates Th17 cells to release IL-17, TNF-α, and GM-CSF which recruitneutrophils to protect the host from invading pathogens (Cuan andKastelein, 2006). In the final phase of infection, this model predictsthat microbe-specific Th1 and Th2 cells would enter the inflamed mucosa.These cells secrete pro-inflammatory factors, which activate macrophagekilling functions and promote anti-microbial antibody responses,respectively. They also repress the differentiation and function of Th17cells. As the infection subsides, IL-6 and IL-23 production is reducedand the balance swings toward favoring the development ofTGF-1-dependent Treg cells, essential for maintaining mucosalhomeostasis.

Role of IL-2

IL-2 has multiple targets. It facilitates differentiation of CD4+ Tcells to Th1 and Th2 cells and expands CD8+ memory T cells and naturalkiller cells. On the other hand, IL-2 promotes apoptosis in antigenactivated T cells. IL-2 also maintains Foxp3+ natural Tregs, expandsthem at high doses and facilitates TGF-β-dependent differentiation ofnaive T cells to inflammatory Th17 cells (Laurence, et al. 2007). Thus,assuming that the main source of IL-2 is activated T cells, there is anegative feedback control of immune responses via IL-2; that is, IL-2produced by activated non-regulatory T cells contributes to themaintenance, expansion and activation of natural Tregs, which in turnlimits the expansion of non-regulatory T cells (Sakaguchi, et al. 2008).Disruption of this IL-2 mediated feedback loop at any step promotes thedevelopment of autoimmune/inflammatory disease. Further, manipulation ofthis feedback loop is instrumental in tuning the intensity of Tregmediated suppression, hence the strength of a variety of immuneresponses.

IL-2 is an essential cytokine for both generating and then limiting Tcell-dependent immune responses. IL-2 has potent T cell growth factoractivity (Smith, 1980). The strongest support for a crucial role forIL-2 in the generation of the immune response is that T cellproliferation and function in vitro can be substantially inhibited usingmonoclonal antibodies specific for either IL-2 or IL-2R (Malek andBayer, 2004). The third key activity of IL-2 is its ability to sensitizeactivated T cells to undergo apoptosis by a tumor necrosis factor (TNF)dependent pathway (Lenardo, 1991).

Impaired production of Treg cells is sufficient to account for thelethal autoimmunity that is associated with IL-2 and IL-2R deficientmice. The main function of IL-2 seems to be the production of Treg cellsand the maintenance of peripheral T cell tolerance (Malek and Bayer,2004). In therapeutic settings, manipulation of Treg cell number orfunction might be accomplished by targeting IL-2 or IL-2R.

Relevant to Canadian Immigrant Population

As described below, an exemplary database comprised of records ofbioassay and patient history information was obtained and used toillustrate various algorithms according to exemplary embodiments of thepresent invention. The individuals whose records were obtained were allimmigrants to Canada. Accordingly, that database is sometimes referredto herein as the “CIP Database.”

Treg cells appear to play a crucial role in controlling the thresholdfor T-cell activation via dendritic cell co-stimulation. When Treg cellactivity is high, as is postulated to occur in response to infection inthe developing world, dendritic cell expression of co-stimulatorymolecule is low (like that on B cells) and production of potentiallyharmful but relatively low affinity self-reactive effector T cells isinhibited. Conversely, as occurs in the developed world where Tregactivity is lower, T cells are more readily activated by dendritic cellsexpressing high levels of co-stimulatory molecules in addition tootherwise harmless antigens, like self determinants and allergens,resulting in the generation of Th1 and Th2 effector T cells,respectively. This way of thinking is attractive because it provides arational explanation for the inverse relationship between the incidenceof autoimmune and allergic diseases on the one hand and infectiousdiseases on the other hand in the developed world (FIG. 4-Basten and deSt Groth, 2008).

Infections Relevant to the Canadian Immigrant Population Database

There is increasing awareness that helminth infections can amelioratepro-inflammatory conditions. The outcome of shistosomal infection inmice depends on Th2 polarization (Belkaid and Rouse, 2005). Theinhibitory effects of natural Treg cells on the Th1 response have beenshown to promote Th2 polarization and to protect the host from lethalinflammatory pathology ((McKee and Pearce, 2004).

Natural Treg cells also seem to be important in the disease caused byhepatitis C virus. A chief complication of this chronic infection ismassive liver damage that often requires organ transplant (Belkaid andRouse, 2005). Liver biopsies obtained at the time of transplants show aninverse correlation between the number of natural Treg cells in theperiphery and the histological inflammatory score. These Treg cells wereactively secreting IL-10 and TGF-β (Cabrera, et al. 2004). Peoplechronically infected with hepatitis C virus have more circulatingnatural Treg cells than do uninfected people, and depletion of Tregcells enhances antigen-specific CD8+ T cell responses in vitro(Sugimoto, et al. 2003).

Schistosomiasis and the Canadian Immigrant Population Database

The estimated mortality owing to Schistosoma mansoni and Schistosomahaematobium in sub-Saharan Africa is 280,000 each year (van der Werf, etal. in press). Schistosomiasis causes a range of morbidities, thedevelopment of which seems to be influenced to a large extent by thenature of the induced immune response and its effects on granulomaformation and associated pathologies in target organs (Pearce andMacDonald, 2002). The development of the immune response in infection isshown in FIG. 5. In the course of the infection, the immune responseprogresses through at least three phases. In the first three to fiveweeks, during which the host is exposed to migrating immature parasites,the dominant response is Th1-like. As the parasites mature, mate, andbegin to produce eggs at weeks 5-6, the response changes; the Th1component decreases and this is associated with the emergence of astrong Th2 response. This response is induced primarily by egg antigens.During the chronic phase of infection, the Th2 response is modulated andgranulomas that form around newly deposited eggs are smaller than atearlier times during infection. During acute illness, there is ameasurable level of TNF-α in the plasma, and PBMCs produce largequantities of IL-1 and IL-6 (de Jesus, et al. 2002). At the end of theTh1 phase, the production of IL-10 is likely at least partly responsiblefor the down-regulation of the inflammatory functions (Montenegro, etal. 1999). In the chronic phase, prolonged Th2 responses contribute tothe development of hepatic fibrosis and chronic morbidity (Cheever, etal. 2000). The main Th2 cytokine responsible for fibrosis is IL-113(Pearce and MacDonald, 2002). Mediators associated with Th1 responses,such as IFN-γ, TNF-α, and IL-12 can prevent IL-13 mediated fibrosis(Hesse, et al. 2001).

Expression of Il-25 (IL-17E) is critical for immunity against helmithinfections (Owyang, et al. 2006). Il-25 protein administration in ananimal model results in elevated expression of Th2 cytokines, IL-4,IL-5, and IL-13 (Fort, et al. 2001). IL-25 also regulates thedevelopment of autoimmune inflammation mediated by IL-17-producing Tcells (Kleinschek, et al. 2007).

Helminth infections in patients with multiple sclerosis (MS) created a Bcell population producing high levels of IL-10, dampening harmfulautoimmune responses. One group concluded that increased production of Bcell-derived IL-10 and of neurotrophic factors are part of theparasite's regulation of host immunity and can alter the course of MS,potentially explaining environmental-related MS suppression observed inareas of low disease prevalence (Correale, et al. 2008).

The burden and chronicity of helminth infections is an importantvariable that may determine whether helminths act as a risk factor for,or confer protection against, allergic diseases (Yazdanbakhsh, et al.2002). The over-riding view is that heavy helminth infections protectagainst allergy (Lynch, et al. 1997). Moreover, it has been proposedthat alterations of commensal bacteria influences intestinal immunehomeostasis by direct regulation of the IL-25/IL-23/IL-17 axis (Zaph, etal. 2008).

Additional Cytokine Assays—CIP Database IL-1

IL-1 is a pleiotropic cytokine the primarily affects inflammatoryresponses, immune reactivity, and hematopoiesis (Dinarello, 2005; Apteand Voronov, 2002). Its potency stems from inducing cytokine, chemokine,pro-inflammatory molecule secretion, and adhesion molecule expression indiverse cells, thereby amplifying and sustaining the response.Membrane-associated IL-1α is immunostimulatory. Low level secreted IL-1βinduces limited inflammatory responses followed by T cell activation.High levels of Il-1β are accompanied by broad inflammation with tissuedamage (Dinarello, 2005; Apte and Voronov, 2002; Mariathasan and Monack.2007).

Interleukin-1 includes a family of closely related genes; the two majoragonistic proteins, IL-1α and IL-1β, are pleiotropic and affect mainlyinflammation, immunity and haemopoiesis (Apte, et al. 2006). These IL-1molecules bind to the same receptors and induce the same biologicalfunctions. As such, they have been considered identical in normalhomeostasis and disease. However, the IL-1 molecules differ in theircompartmentalization within the cell. IL-1β is active in its secretedform, while IL-1α is active in cell-associated forms—either as theintracellular precursor or as membrane-bound IL-1α. It has been proposedthat membrane associated IL-1α expressed on malignant cells stimulatesanti-tumor immunity, while secretable IL-1β, derived from themicroenvironment or malignant cells, activates inflammation thatpromotes invasiveness and also induces tumor-mediated suppression (Apte,et al. 2006). Both sarcoma cell-derived IL-1α and IL-1β promote tumorgrowth. However, IL-1α exerts regulatory authority on the tumorcell-matrix cross-talk, and only IL-1β initiates systemic inflammation(Nazarenko, et al. 2008).

IL-1 is also an important mediator of inflammation and a major cause oftissue damage in rheumatoid arthritis (RA). In a mouse model ofvaccination to prevent RA, it was found that immunization with IL-1β wasstrongly protective against the development of arthritis, whileimmunization with a similarly constructed IL-1α vaccine had no effect(Spohn, et al. 2008). Another group examining genetic polymorphisms ingenes coding for IL-1α and IL-1β in RA found that in a majority ofcases, genetic polymorphisms in these genes were not a major contributorto genetic susceptibility (Johnsen, et al. 2008), strengthening theargument for ImmunoScore based analyses which measures phenotypicexpression of protein components.

IL-8

Interleukin-8 (IL-8 or CXCL8) is a chemokine known to possesstumorigenic and pro-angiogenic properties as well as leukocytechemotactic activity (Brat, et al. 2005). Il-8 has been found to play animportant role in autoimmune, inflammatory, and infectious diseases(Harada, et al 1994; Koch, et al. 1992; Smyth, et al. 1991). Because ofits potent pro-inflammatory properties, IL-8 is tightly regulated, andits expression is low or undetectable in normal tissues. Expression ofIL-8 can be induced by IL-1, TNF-α, IL-6, and IFN-γ (Baggiolini, et al.1994; DeForge, et al. 1993). Potent inhibitors of IL-8 productioninclude IL-4 and IL-10 (Mukaida, et al. 1994; Xie, 2001).

There is evidence that IL-8 is involved in tumor formation and malignantprogression (Brat, et al. 2005). Mast cell mediators includingfibroblast growth factor-2 and IL-8 are mitogenic to melanoma cells.Current evidence supports an accessory role for mast cells in thedevelopment and progression of cutaneous malignancies, but it iscurrently unclear whether the mast cells have promoting or inhibitoryeffects on tumors (Ch'ng, et al. 2006).

IL-8 is expressed in working muscles. The small transient release ofIL-8 by working muscle is likely used for local angiogenesis, whereassystemic increase of plasma IL-8 is likely indicative of a tumor diseasestate (Akerstrom, et al. 2005). Other cytokines, including Il-6 andIL-15 are released locally by working muscle (Nielsen and Pedersen,2007). Interestingly, anti-oxidant vitamins, C and E, have been shown toinhibit the release of pro-inflammatory cytokines from human skeletalmuscle (Fischer, et al. 2004).

The use of biochemical markers in neonatal infection has remained animportant area of research. Many infection markers are components of theinflammatory cascade and IL-6 and IL-8 have been demonstrated to havegood diagnostic utility as early phase markers, while CRP andprocalcitonin have superior diagnostic properties during the laterphases (Lam and Ng, 2008).

IL-17

Interleukin-17, described extensively above, is a pro-inflammatorycytokine which induces differentiation and migration of neutrophilsthrough induction of cytokines and chemokines includinggranulocyte-colony stimulating factor and CXCL8/IL-8. IL-17 producing Tcells have a pivotal role in the pathogenesis of autoimmune diseases.IL-17 is also involved in protective immunity against extracellularbacterial or fungal pathogens such as Klebsiella pneumoniae and Candidaalbicans (Matsuzaki and Umemura, 2007).

IL-13

Interleukin-13 plays a major role in various inflammatory diseasesincluding cancer, asthma, and allergy. It mediates a variety ofdifferent effects on various cell types including B cells, monocytes,natural killer cells, and fibroblasts (Joshi, et al. 2006).

Bronchial asthma is a complex disorder that is thought to arise as aresult of aberrant T cell responses to non-infectious environmentalantigens. In particular, asthma symptoms are closely associated with thepresence of activated Th2 cytokine-producing cells making IL-4, IL-5,and IL-13 in the airway wall (Nakajima and Takatsu, 2007). Animal modelsof disease have provided compelling evidence that IL-13, independent ofthe other Th2 cytokines, is both necessary and sufficient to induce allfeatures of allergic asthma (Wills-Karp, 2004). IL-13 has been describedas a target for therapeutics, as it is involved in the pathogenesis ofbronchial asthma and therapeutic agents have been described that blockIL-13 signals (Izuhara, et al. 2006). Biological agents directed againstthe IL-13 pathway and new immunoregulatory agents that modulatefunctions of Treg and Th17 cells are likely to be successful againstasthma (Adcock, et al. 2008).

Th2 cells, producing IL-4 and IL-13 have also been implicated insystemic sclerosis which is characterized by extensive fibrosis,microvascular stenosis, and autoantibody production (Sakkas, et al.2006).

Receptors for IL-4 and IL-13 are overexpressed on malignant cells frombrain tumors. These cells have been experimentally targeted by using achimeric IL-13 constructed with a mutated form of pseudomonas exotoxin(Shimamura, et al. 2006). ImmunoScore technology would be usefulmonitoring clinical therapies and overall health of the immune systemduring the course of such treatments.

In tuberculosis, IL-4 and IL-13 can undermine effective Th1-mediatedimmunity and make an individual more susceptible to TB infection (Rook,2007). It has been postulated that for a TB vaccine to be effective, notonly must the Th1 axis be promoted, the Th2 axis must be suppressed.Understanding the balance between Il-12, IL-13, Il-23, and IL-27 iscrucial to the development of immune intervention in tuberculosis(Cooper, et al. 2007).

IL-13 levels have been shown to be elevated in patients withgastrointestinal nematode and helminth infections of the liver (Grencisand Bancroft, 2004; Hirayam, 2004). In patients with schistosomiasis,both IL-13 and IFN-γ were shown to be elevated, suggesting acompartmentalization of the anti-schistosome immune response (Dessein,et al. 2004).

IL-15

Interleukin-15 is a pleiotropic cytokine that plays an important role inboth the innate and adaptive immune system. IL-15 promotes theactivation of neutrophils and macrophages, and is critical to DCfunction. In addition, IL-15 is essential to the development,homeostasis, function, and survival of natural killer and CD8+ T cells(Diab, et al. 2005). Abnormalities of IL-15 expression have beendescribed in patients with rheumatoid arthritis or inflammatory boweldisease (Waldmann, 2002). In contrast to the role of IL-2 which is inthe elimination of self-reactive T cells to prevent autoimmunity, IL-15is dedicated to the prolonged maintenance of memory T cell responses toinvading pathogens (Waldmann, 2006). IL-15 has been proposed as havinganti-cancer properties, in addition to triggering innate immunity(Shanmugham, et al. 2006).

TNFβ (Lymphotoxin β)

Lymphotoxin β is implicated in lymphoid follicle development, productionof pro-inflammatory cytokines, and can enhance the production offibroblasts and synoviocytes. The expression of lymphotoxin β issignificantly increased in RA patients (O'Rourke, et al. 2008). It wasspeculated that lymphotoxin β may play a role in RA disease pathogenesisby contributing to a more intense inflammatory reaction in the synovium.

Important to the concept of immunosenescence, in a mouse model,over-expression of lymphotoxin was shown to induce fulminant thymicinvolution (Heikenwalder, et al. 2008). Host responses tocytomegalovirus (CMV) infections include early initial production ofinterferons. New data indicate that, preceding the induction of type Iinterferons, an earlier critical type I interferon elicited in primaryinfected stromal cells via the lymphotoxin β receptor system andmediated by B cells is necessary to kick-start an efficient antiviralresponse (Fodil-Cornu and Vidal, 2008).

In addition, signaling through the lymphotoxin pathway is a crucialelement in the maintenance of the organized microenvironment. Inhibitorsof the lymphotoxin pathway have been shown to reduce disease in a widerange of autoimmune models (Gommerman and Browning, 2003).

ImmunoScore Total Immunoglobulin Assays

IgG (g/L)

-   -   IgG1 (normal range=4.9-11.4 g/L)    -   IgG2 (normal range=1.5-6.4 g/L)    -   IgG3 (normal range=0.2-1.10 g/L)    -   IgG4 (normal range=0.08-1.4 g/L)        IgM (g/L)        IgA (g/L)        IgE (g/L)

ImmunoScore Cytokine Assay Panel Th1

-   -   IL-12 (induction)    -   IL-27 (induction)    -   IFN-γ (produced by Th1)    -   TNF-α (produced by Th1)    -   IL-2 (produced by Th1 late in cycle?)    -   IL-10 (Th1 suppressed by)    -   TGF-β (Th1 suppressed by)

Th2

-   -   IL-4 (induction and production by Th2)    -   IL-5 (produced by Th2)    -   IL-13 (produced by Th2)    -   IL-10 (produced by Th2 to dampen Th1 response?)    -   IL-10 (suppressed by)    -   TGF-β (suppressed by)

Th17

-   -   TGF-β (induced by—with IL-6)    -   IL-6 (induced by—with TGF-β)    -   IL-1 (induced by)    -   IL-17A (produced by Th17)    -   IL-17F (produced by Th17)    -   IL-21 (produced by Th17)    -   IL-22 (produced by Th17—seen as both inflammatory and        anti-inflammatory)    -   IL-23 (Th17 response maintained by presence of IL-23)    -   IL-1 (Th17 response maintained by IL-1 which is produced by Th1        cells)    -   IL-4 (suppressed by IL-4 which is produced by Th2 cells)    -   IFN-γ (suppressed by IFN-γ which is produced by Th1 cells)    -   IFN-α (suppressed by)    -   IL-2 (suppressed by)    -   IL-27 (suppressed by)

Treg

-   -   TGF-β (induced by—in the absence of IL-6)    -   IL-10 (produced by—suppress Th1 and Th2 responses)    -   TGF-(produced by—suppress Th1 and Th2, expand Th17 together with        IL-6)    -   IL-6 (suppressed by)    -   IL-21 (suppressed by)    -   IL-31 (suppressed by)

B Cell Differentiation and Regulation

-   -   IL-2    -   IL-4    -   IL-7 (important in aging immune system)    -   IL-9    -   IL-10    -   IL-15    -   IL-21

Canadian Immigrant Population Database Cytokine Assays

-   -   IL-1α—inflammatory; likely stimulates Th17 response locally    -   IL-1β—inflammatory; likely stimulates Th17 response more        systemically    -   IL-2—produced by Th1—induce Treg    -   IL-4—produced by Th2    -   IL-5—produced by Th2    -   IL-6—pro-inflammatory—together with TGF-β induce Th17    -   IL-8—pro-inflammatory    -   IL-10—anti-inflammatory; produced by Treg    -   IL-12—induce Th1    -   IL-13—produced by Th2    -   IL-15—trigger of innate immunity; anti-tumor role    -   IL-17—pro-inflammatory; produced by Th17    -   IL-23—maintenance of Th17    -   IFN-γ—produced by Th1    -   TNF-α—pro-inflammatory; produced by Th17 and Th1    -   TNF-β—pro-inflammatory; increased levels signal autoimmune        disease flares

FIG. 5B depicts T helper cell commitment towards specific lineages.Depending on local cytokine milieu, naive CD4+ cells can differentiateto one of three types of CD4+ T effector cells (Th1, Th2, or Th17) orCD4+ immunosuppressive Treg cells. Green arrows indicate positivecytokine signals for differentiation, while red arrows indicatesuppressive effects of cytokines on particular cell types. Thissimplified diagram captures only the cytokines of major influence as thestate of the art currently stands.

FIG. 5C depicts Th1/Th2 Paradigm. Model of paradigm as it existed circa2000. Th1 cells known for important role in cell mediated immunity,whilst Th2 cells acknowledged to be important for humoral immunity. Atthis time, it was thought that Th1 over-response was solely responsiblefor autoimmune disease. The story has proven to be more complicated withthe current understanding of the role of Th17 cells.

FIG. 5D depicts an evolving Th1/Th2/Th17/Treg paradigm. The Th1/Th2paradigm now includes arms that recognize the importance of Th17 andTreg cells. Rather than balance on one fulcrum between two oppositesides, the model now encompasses more complicated interactions whilsttilting on at least two axes. Over-expression of any one of the fourarms of the T cell immune response without response of the oppositefunctions can lead to undesirable complications and over-reaction of theimmune system. Th17 responses coupled with Th1 responses can lead toautoimmune reactions, while Th17 coupled to Th2 responses can lead toallergic reactions. Over-expression of regulatory responses with Th1reactions can lead to chronic microbial or viral infection, whilecoupled with Th2 responses can lead to chronic parasitic infections.

FIG. 5E depicts an exemplary model illustrating how Treg-mediatedcontrol of CD80/CD86 expression may control the threshold of antigenrecognition, crucial for preventing the activation of low avidityself-reactive T cells that are below the cut-off imposed during thymicselection. Treg cells stimulated during high affinity responses tomicrobes would increase the threshold (indicated by green line) byreducing dendritic cell expression of co-stimulatory molecules.Conversely, in the absence of strong Treg cell activity, the thresholdof self antigen recognition may drop below the thymic cut-off (indicatedby black line), allowing activation of low avidity anti-self T cells(Basten, et al. 2008).

FIG. 5F depicts the development of the immune response in schitosomeinfection (Pearce and MacDonald, 2002).

4. Quantitation

As part of the ImmunoScore technology, immune responses can bequantitated. For example, Th1, Th2, Th17, and Treg responses can bequantitated. These quantitative values can be used in at one point intime, or can be trended over time to help determine a person's immunestatus. These values and/or their time responses can also be used withother factors such as antibody concentrations to help determine aperson's immune status.

Immune responses such as Th1, Th2, Th17, Treg, and the like can bequantitated using a collection of blood measurements; for example, acollection of cytokine concentrations. Some of cytokines suppress animmune response; suppression can be, for example, quantitativelycharacterized by negative coefficients in a function relatingconcentrations to response. Because cytokine concentrations can vary bymany orders of magnitude, immune response quantitation may be related tothe logarithm of the concentrations rather than the concentration.Because basal concentrations of cytokines are not always the same, theratio of the cytokine concentration to its basal concentration (or thelogarithm of the ratio) can be used in the quantitation.

As more particular examples, the Th1 quantitation can be a function ofcytokine concentrations; the Th1 quantitation can be a function of theconcentrations of IFN-γ, TNF-α, IL-2, IL-12, IL-10, TGF-β, and IL-23;the Th1 quantitation can be a polynomial function of the concentrations(or the logarithm of the concentrations) of IFN-γ, TNF-α, IL-2, IL-12,IL-10, TGF-β, and IL-23; the Th1 quantitation can be a linear functionof the concentrations (or the logarithm of the concentrations) of IFN-γ,TNF-α, IL-2, IL-12, IL-10, TGF-β, and IL-23. As a second set ofexamples, the Th2 quantitation can be a function of cytokineconcentrations; the Th2 quantitation can be a function of theconcentrations of IL-4, IL5, IL-13, IL-10, and TGF-β; the Th2quantitation can be a polynomial function of the concentrations (or thelogarithm of the concentrations) of IL-4, IL5, IL-13, IL-10, and TGF-β;the Th2 quantitation can be a linear function of the concentrations (orthe logarithm of the concentrations) of IL-4, IL5, IL-13, IL-10, andTGF-β. As a third set of examples, the Th17 quantitation can be afunction of cytokine concentrations; the Th17 quantitation can be afunction of the concentrations of TGF-β, IL-2, IL-4, IL-6, IL-17, IL-21,IL-22, IL-23, IFN-α, and IFN-γ; the Th17 quantitation can be apolynomial function of the concentrations (or the logarithm of theconcentrations) of TGF-β, IL-2, IL-4, IL-6, IL-17, IL-21, IL-22, IL-23,IFN-α, and IFN-γ; the Th17 quantitation can be a linear function of theconcentrations (or the logarithm of the concentrations) of TGF-β, IL-2,IL-4, IL-6, IL-17, IL-21, IL-22, IL-23, IFN-α, and IFN-γ. As a fourthset of examples, Treg quantitation can be a function of cytokineconcentrations; the Treg quantitation can be a function of theconcentrations of IL-2, IL-6, IL-10, IL-31, IL-35, and TGF-β; the Tregquantitation can be a polynomial function of the concentrations (or thelogarithm of the concentrations) of IL-2, IL-6, IL-10, IL-31, IL-35, andTGF-β; the Treg quantitation can be a linear function of theconcentrations (or the logarithm of the concentrations) of IL-2, IL-6,IL-10, IL-31, IL-35, and TGF-β.

Combining the quantitative measurements for Th1 and Th2 responses toform a Th1|Th2 response is another aspect of ImmunoScore technology. Insome embodiments, Th1|Th2 is simply the difference between the Th2quantitation and the Th1 quantitation. For example, a positive Th1|Th2value can be indicative of a Th2 response, while a negative Th1|Th2value can be indicative of a Th1 response.

Combining the quantitative measurements for Th17 and Treg responses toform a Th17|Treg response is another aspect of ImmunoScore technology.In some embodiments, Th17|Treg is simply the difference between the Th17quantitation and the Treg quantitation. For example, a positiveTh17|Treg value can be indicative of a Th17 response, while a negativeTh17|Treg value can be indicative of a Treg response.

Further distillation of immune response information can be done, forexample, by combining the Th1|Th2 response and the Th17|Treg response.By considering Th1|Th2 and Th17|Treg as two dimensions of an immuneresponse, a magnitude and direction can be computed. A small magnitudecan be interpreted as the immune system being in balance. For largemagnitudes, the direction can indicate the type of immune response.

For example, if 0° represents a Th2 response; 90° a Th17 response; 180°,a Th1 response; and 270°, a Treg response; then a direction of 45° canbe indicative of allergies and/or fibrosis. Continuing this example,135° can be indicative of autoimmunity and/or an acute bacterialinfection; 225° can be indicative of chronic protozoan & mycobacterialinfections; 315° can be indicative of Helminth infections. Trendtracking magnitude and direction over time can be used to differentiatebetween acute and chronic immune issues, such as the 135° direction thatmay represent autoimmunity or an acute bacterial infection.

As additional knowledge about the immune system is learned, additionaldimensions can be added to the two dimensional Th1|Th2, Th17|Tregexample above. Small magnitudes can still represent a balanced immunesystem, and for large magnitudes the direction can indicate the type ofimbalance.

Example 1

Let Th1 be the quantitative measurement of the Th1 response.

$\overset{\_}{{Th}\; 1} = {\alpha_{0} + {\sum\limits_{i}{\alpha_{i}\ln \; c_{i}}}}$

In the above equation, c_(i) represents the concentration of the i^(th)cytokine, α_(i) s the coefficient relating the magnitude and sign of theamount the i^(th) cytokine affects the Th1 response, and α₀ is aweighted sum of the logarithm of the basal concentrations. Cytokinesthat do not affect the Th1 response can either be excluded from thesummation or use a coefficient of 0. To simplify notation when combiningresponses, the coefficient of 0 method will be used. This equation is alinear function of logarithm of the ratio of the cytokine concentrationto the basal concentration. To change the equation to a linear functionof logarithm of the cytokine concentration, set α₀=0.

Similar equations can be generated for the Th2 response, Th17 response,and the Treg response:

$\quad\begin{matrix}{\overset{\_}{{Th}\; 2} = {\beta_{0} + {\sum\limits_{i}{\beta_{i}\ln \; c_{i}}}}} \\{\overset{\_}{{Th}\; 17} = {\gamma_{0} + {\sum\limits_{i}{\gamma_{i}\ln \; c_{i}}}}} \\{\overset{\_}{T\; {reg}} = {\delta_{0} + {\sum\limits_{i}{\delta_{i}\ln \; c_{i}}}}}\end{matrix}$

The Th1|Th2 response can then be computed as

The Th17|Treg response can be computed as

The magnitude of the combined response can be computed as

$\sqrt{\left( {\beta_{0} - \alpha_{0} + {\sum\limits_{i}{\left( {\beta_{i} - \alpha_{i}} \right)\ln \; c_{i}}}} \right)^{2} + \left( {\gamma_{0} - \delta_{0} + {\sum\limits_{i}{\left( {y_{i} - \delta_{i}} \right)\ln \; c_{i}}}} \right)^{2}}$

The direction of the combined response can be computed as

$\tan^{- 1}\frac{\gamma_{0} - \delta_{0} + {\sum\limits_{i}{\left( {\gamma_{i} - \delta_{i}} \right)\ln \; c_{i}}}}{\beta_{0} - \alpha_{0} + {\sum\limits_{i}{\left( {\beta_{i} - \alpha_{i}} \right)\ln \; c_{i}}}}$

When tracking the direction over time, the solution to the arctangentthat minimizes the magnitude of the direction change can be selected:for example, if the first direction is 1° and the direction changes by2° in the clockwise direction, −1° should be chosen rather than 359°.

C. Immunoscore Exemplary Superpanels 1. ImmunoScore Diagnostic Panel andPreventive Therapy for Autoimmune Disease

In exemplary embodiments of the present invention, some or all of thefollowing assays can be included in an ImmunoScore AutoimmuneScreening/Diagnostic Panel:

1. Antibody Assays

-   -   anti-myelin oligodendrocyte glycoprotein (MOG) antibody    -   anti-measles virus antibodies    -   anti-21-hydroxylase antibody    -   anti-adrenal cortex antibody    -   anti-Klebsiella antibodies    -   anti-cardiolipin antibody    -   anti-lupus anticoagulant antibody    -   anti-beta-2-glycoprotein antibody    -   anti-hematopoietic precursor cell antibodies    -   anti-soluble liver antigen antibody    -   anti-RO/SSA antibody    -   anti-endomysial antibody (AEA)    -   anti-tissue transglutaminase (anti-tTG)    -   anti-Saccharomyces cerevisiae antibody (ASCA)    -   anti-neutrophil antibody (PANCA)    -   anti-porin protein C of E. coli antibody (anti-OmpC)    -   anti-glutamic acid decarboxylase antibody (GADA)        -   particularly anti-65 kDa isoform    -   anti-protein tyrosine phosphatase-like molecule antibody (IA-2A)    -   anti-glomerular basement membrane (GBM) antibody    -   anti-neutrophil cytoplasmic antigens (ANCA)    -   anti-GD1a/GD1b complex antibody    -   anti-LM1 antibody    -   anti-GM1 antibody    -   anti-thyroglobulin antibody    -   anti-nuclear antibodies (ANA)        -   lupus anticoagulant (LA) antibody        -   anti-phospholipid (aPL)        -   anti-SS/A antibody        -   anti-SS/B antibody        -   anti-Sm antibody        -   anti-RNP antibody        -   anti-Jo1 antibody        -   anti-Scl-70 antibody        -   anti-dsDNA antibody        -   anti-Centromere B antibody        -   anti-Histone antibody    -   anti-alphaIIbbeta 3 IgM    -   anti-acetylcholine receptor (anti-AChR) antibody    -   anti-muscle-specific tyrosine kinase (MuSK) antibody    -   anti-neuroleukin antibody    -   anti-gliadin antibody    -   anti-CV 2 antibody    -   anti-GQ1b IgG    -   anti-GQ1b IgM    -   anti-thyroid peroxidase antibody    -   keratinocyte cell-surface antibodies        -   anti-BP 180 (bullous pemphigoid antigen 2)        -   anti-BP 230 (bullous pemphigoid antigen 1)    -   anti-intrinsic factor antibody    -   anti-parietal cell antibodies    -   anti-mitochondrial antibodies        -   in particular, anti-E2 component of pyruvate dehydrogenase            complex (PDC) antibody    -   anti-cyclic citrullinated peptide (CCP) antibody    -   anti-heat shock protein (HSP) 65 antibody    -   anti-HSP 90 antibody    -   anti-DnaJ antibody    -   anti-BiP antibody    -   anti-heterogeneous nuclear RNP A2/B1 antibody    -   anti-heterogeneous nuclear RNP D antibody    -   anti-annexin V antibody    -   anti-calpastatin antibody    -   anti-type II collagen antibody    -   anti-glucose-6-phosphate (GPI) antibody    -   anti-elongation factor    -   anti-human cartilage gp39 antibody    -   anti-Chlamydia antibodies    -   anti-La/SSB antibody    -   anti-fodrine antibody    -   anti-salivary duct antibodies    -   anti-Red Blood Cell (RBC) IgM    -   anti-neutrophil cytoplasmic antibodies    -   anti-thyroid microsomal antibody (ATMA)    -   anti-smooth muscle antibody (SMA)    -   anti-mitochondrial antibody (AMA)    -   anti-extractable nuclear antigens (ENA) antibody    -   anti-actin antibody (AAA)    -   anti-hair follicle antibodies        -   anti-anagen matrix antibody        -   anti-cuticle antibody        -   anti-cortex keratinocytes antibody        -   anti-melanocyte nuclear antigen    -   anti-human dermal microvascular endothelial cells (HDMEC)        antibodies        -   anti-81 kDa HDMEC antigen, in particular    -   anti-Trypanosoma cruzi antibodies    -   anti-oleic acid IgM    -   anti-palmitic acid IgM    -   anti-myristic acid IgM    -   anti-azelaic acid IgM    -   anti-malondialdehyde IgM    -   anti-aceylcholine IgM    -   anti-5-farnesyl-L-cysteine IgM    -   anti-ganglionic nicotinic acetylcholine receptor antibody    -   anti-follicle-stimulating hormone (FSH) IgA    -   anti-V14D IgA    -   anti-V14D IgG    -   anti-cytoskeleton-associated protein 4/p63 (CKA4/p63)-specific        antibody    -   anti-cytokeratin 10 antibody    -   anti-Voltage-Gated Potassium Channels (VGKCs) antibodies    -   anti-Chlamydia pneumoniae antibodies    -   anti-human cytomegalovirus (CMV) antibodies    -   anti-Toxoplasma gondii antibodies    -   anti-CENP-A antibody    -   anti-CENP-B antibody

2. Cytokine Assays

-   -   Interleukin-1α (IL-1α)    -   IL-1β    -   IL-2    -   IL-4    -   IL-5    -   IL-6    -   IL-7    -   IL-8    -   IL-10    -   IL-12    -   IL-13    -   IL-15    -   IL-18    -   Interferon α (IFN-α)    -   IFN-γ    -   TNF-α    -   G-CSF    -   MCP-1    -   MIP-1α    -   MIP-1    -   MIP-3α    -   MIP-3β    -   EGF    -   VEGF    -   TNFRII    -   EGFR

3. Toll-Like Receptor (TLR) Genetic Variants

-   -   TLR 2    -   TLR 3    -   TLR 4    -   TLR 7    -   TLR 8    -   TLR 9

4. HLA Haplotype Screening

-   -   HLA A24    -   HLA B8    -   HLA B18    -   HLA B27    -   HLA B51    -   HLA B60    -   HLA B62    -   HLA DR2    -   HLA DR3    -   HLA DR4    -   HLA DR5    -   HLA DR7

5. Protein Isoforms/Genetic Polymorphisms/Serum Protein Levels

-   -   Apolipoprotein E isoforms        -   apo E2        -   apo E3        -   apo E4    -   Serum Apolipoprotein A-IV level    -   Mannose-binding lectin (MBL) polymorphism    -   Serum Haptoglobin level    -   Serum Transthyretin level    -   Serum Fibrinogen level    -   Serum Vitamin B12 level    -   Serum Folic acid level

2. ImmunoScore Diagnostic Panel: Aging, Longevity, Cancer and HumanCytomegalovirus

Old age is accompanied by an increased incidence of infection and poorerresponses to vaccination. A progressive decline in the integrity of theimmune system is one of the physiologic changes during mammalian aging.Perhaps the most profound clinical impact of age on the immune systemconcerns the response of the elderly to vaccination (Pawelec, 2005). Animmune risk phenotype (IRP) was described wherein individuals possessedhigh CD8 and low CD4 numbers and poor proliferative response (Wikby, etal. 2005). Characteristics of the IRP are listed in Table I (Vasto, etal. 2007). The IRP consists of a cluster of these parameters, not eachparameter individually. Which are the most important and whichadditional factors are involved remains to be determined.

Lifelong and chronic antigenic load may represent the major drivingforce for immunosenescence, which impacts on human lifespan by reducingthe number of virgin antigen-non experienced T cells, and results intheir replacement by expanded clones of antigen-experienced effector andmemory T cells which display a late differentiation phenotype.Gradually, the T cell population shifts to a lower ratio of naïve cellsto memory cells, the thymus releases fewer naïve T cells with age andthose T cells remaining, especially the CD8⁺ subset, also show increasedoligoclonality with age. Presumably, the repertoire of cells availableto respond to antigenic challenge from previously encountered pathogensshrinks. In addition, older organisms often are overrun by memory cellsthat carry a single type of T cell receptor, i.e. the clonal expansionreferred to above. Therefore, the memory cells from old individualsmight recognize a limited set of antigens despite being plentiful innumber, and in addition, are likely to show various degrees ofdysfunctionality. Many of the clonal expansions filling the individual'simmune system seem to result from previous infections by persistentviruses, especially CMV (Ouyang, et al. 2003b), but also, to a lesserextent EBV (Ouyang, et al. 2003a) and possibly other herpes viruses(Vasto, et al. 2007). A high number of CD8⁺ cells are found to bespecific for a single CMV epitope (Pawelec, et al. 2005; Pawelec, et al.2004). In humans, the accumulation of CMV-specific T cells has beenobserved to reduce T cell immunity toward EBV infection (Khan, et al.2004) and influenza vaccination (Trzonkowski, et al. 2003). Functionalanalyses performed with T cells from nonagenarians demonstrated thatthey were characterized by decreased functional capacity when comparedwith similar cells isolated from middle aged individuals (Hadrup, et al.2006). This suggests that increased numbers of CMV-specific T cellscould be the result of a compensatory mechanism enabling control of CMVdespite lower functional capacity (Hadrup, et al. 2006). The biology ofCMV infection in humans can be conceptualized as an evolutionary“negotiated” balance between viral mechanisms of pathogenesis,persistence, and immune evasion and the host cellular immune response(Sylwester, et al. 2005).

One of the immunodominant viral antigens recognized by CMV-specific CD8⁺T cells is derived from the 65-kDa phosphoprotein (pp 65). Samples fromoctogenarian and nonagenarian populations revealed that a large numberof CD8⁺CD28⁻ cells were specific for the pp 65 antigen. These findingsimply a co-dominant role of CMV as a cause for a compromised immunity inold age (Vasto, et al. 2007). A second immunodominant antigen is theIE-1 antigen. Epitope specificity and immunodominance of CD8 T cellsagainst IE-1 and pp 65 are comparable in acute infection and long-termmemory often with marked focusing of responses that are probablyestablished very early on. However, the kinetics of CD8 T cell responsesfor these antigens expressed at opposite ends of the replicative cycleof the virus reflect the different modes of antigen presentation, whichprobably depend on levels of viral activity occurring over the lifetimeof the host (Khan, et al. 2007). Other studies have suggested anextraordinary complexity of CMV-specific T cell responses to chronicinfection (Sylwester, et al. 2005). This complexity complicates effortsto understand the basis of the CMV immune balance and, in clinicalpractice, to determine the thresholds that define the boundary betweencontrolled vs. progressive CMV infection in immunocompromised subjectsand between normal and excessive CMV-specific immunity in the elderly(Sylwester, et al. 2005).

There are a suggested sequence of stages for IRP individuals that beginwith the acquisition of CMV infection in earlier life, followed bygeneration of CD8⁺CD28⁻ cells to control persistent CMV infection, andeventually the development of an IRP. Recently, a group of rareindividuals was discovered who moved out of the IRP category by aprocess of immune suppression, including increases in IL-6 and IL-10 anddecreases in the number of CD3⁺CD8⁺CD28⁻ cells (Wikby, et al. 2006).

There are two theories regarding the evolution of senescence—mutationaccumulation and antagonistic pleiotropy. The mutation accumulationtheory of senescence postulates that there are numerous loci subject tomutation to deleterious alleles, whose effects on survival or othercomponents of fitness are restricted to narrow bands of ages (Rose,1991). The equilibrium frequencies of such deleterious alleles will behigher the later in life in which they act (Charlesworth, 1994). Thealternative path involves antagonistic pleiotropy, according to whichgenes that increase early performance are likely to become establishedin a population even if they have adverse effects on later performance(Williams, 1957; Rose, 1991). Antagonistic pleiotropy was originallydefined as meaning opposite effects of the same allele at different ages(Williams, 1957). Antagonistic pleiotropy in evolutionary theory usuallyrefers to opposite effects of a genotype on fecundity and survival. Theexistence of trade-offs between these two components of Darwinianfitness was proposed to explain the evolution of senescence and themaintenance, via the creation of the heterozygous advantage, ofpolymorphism at loci involved in the determination of both traits(Kirkwood and Rose, 1991). In a later model, antagonistic pleiotropyinvolved, instead, relative survival values of a genotype at differentages (Toupance, et al. 1998). The two theories are not mutuallyexclusive, and modeling exercises have examined the validity of each(Charlesworth and Hughes, 1996).

An example of antagonistic pleiotropy would be the high expression oftestosterone in a male gorilla, which could lead to increased aggressionand strength that would allow the male to become dominant and mate morefrequently, but may eventually lead to a shortened lifespan due toincreased atherosclerosis. Recent studies at the molecular level havesuggested that cellular senescence may be antagonistically pleiotropicbecause it prevents tumorigenesis, but also contributes to organismicaging (Troen, 2003).

In one study, it was suggested that cellular senescence wasantagonistically pleiotropic, protecting from cancer early in life, butpromoting carcinogenesis in aged organisms (Krtolica, et al. 2001).Another study (Hughes, et al. 2002) found the AP (antagonisticpleiotropy) model is consistent with the existence of a few genes withindividually large effects on late-life fitness, whereas the MA(mutation accumulation) process should lead to the maintenance of maydeleterious alleles at intermediate frequencies within populations andthese alleles can have individually small effects on late-lifeperformance and health. Current methods of identifying aging genes (suchas mutation studies and quantitative trait locus-mapping experiments)are most effective in finding alleles of large effect, and even welldesigned studies will probably miss genes with small effects. Novelapproaches are needed to find such genes.

Cancer rates also increase sharply with age in both sexes, and themajority of cases of cancer occur in patients over the age of 65. Tumorprogression is a complex process that depends on interactions betweentumor cells and host cells. The inflammatory aspect of the host responseis of particular interest because it includes the release ofpro-inflammatory cytokines, some of which may promote tumor growth andhence influence survival. Some kinds of solid tumors are likely affectedby regulatory cytokine genotypes. In particular, pro-inflammatorygenotypes characterized by a low IL-10 or a high IL-6 producer seem tobe associated with a worse clinical outcome (Caruso, et al. 2004). Onthe other hand, recent evidence has linked IL-10 and IL-6 cytokinepolymorphisms to longevity. In fact, individuals who are geneticallypredisposed to produce high levels of IL-6 have a reduced capacity toreach the extreme limits of human life, whereas the high IL-10 producergenotype is increased among centenarians (Caruso, et al. 2004). Theopposite effect of IL-6 and IL-10 in cancer and longevity is intriguing.Inflammatory genotypes may be both friends and enemies. The immunesystem has evolved to control pathogens, therefore pro-inflammatoryresponses are likely to be evolutionarily programmed to resist fatalinfections, and a high IL-6 or a low IL-10 production is associated withincreased resistance to pathogens. However, decreased level of IL-6 orincreased level of IL-10 might better control inflammatory responses andcancer development. These conditions might result in an increased chanceof long life survival in an environment with reduced pathogen loads(Caruso, et al. 2004).

Most tumor suppressor genes can be classified as either caretakers orgatekeepers (Kinzler and Voglestein, 1997). Caretaker tumor suppressorgenes prevent cancer by protecting the genome from mutations. Theygenerally act by preventing DNA damage or optimizing DNA repair. Inaddition to preventing cancer, genes that help maintain genomicintegrity also prevent or retard the development of other agingphenotypes and age-related pathologies (Hasty, et al. 2003). Gatekeepertumor suppressors, by contrast, prevent cancer by acting on intactcells—specifically, mitotic cells that are at risk for neoplastictransformation. Gatekeepers can virtually eliminate potential cancercells by inducing programmed cell death (apoptosis). Alternatively, theycan prevent potential cancer cells from proliferating by inducingpermanent withdrawal from the cell cycle (cellular senescence). Althoughlittle is known about how cells choose between apoptotic and senescenceresponses, there is little doubt that both responses are crucial forsuppressing cancer (Campisi, 2001; Green and Evan, 2002).

Increasing evidence suggests that the rise in cancer with age resultsfrom a synergy between the accumulation of mutations and age-related,pro-oncogenic changes in the tissue milieu. Most age related cancersderive from epithelial cells. Epithelial tissues are supported by astroma, which is composed of extracellular matrix and several celltypes. One age-related change that occurs in epithelial tissues is theaccumulation of senescent cells. Cellular senescence is a potent tumorsuppressive mechanism that irreversibly arrests proliferation inresponse to damage or stimuli that put cells at risk for neoplastictransformation. Senescent cells secrete factors that can disrupt tissuearchitecture and stimulate neighboring cells to proliferate. Thesuggestion has been made that senescent cells can create a tissueenvironment that synergizes with oncogenic mutations to promote theprogression of age-related cancers (Krtolica and Campisi, 2003). Therecent evidence indicates that cellular senescence may be an example ofevolutionary antagonistic pleiotropy.

A major difference between microbial pathogens and tumors as potentialvaccine targets is that cancer cells are derived from the host, and mostof their macromolecules are normal self-antigens present in normalcells. To take advantage of the immune system's specificity, antigensmust be found that clearly mark the cancer cells as different from hostcells. An area generating much interest is the possibility of overcomingmechanisms that downregulate or attenuate the immune response, as isdepicted in FIG. 5D (Berzofsky, et al. 2004b). With reference thereto,FIG. 5D illustrates negative regulation of tumor immunosurveillance andantitumor immune responses. FIG. 5D(A) depicts CD4⁺CD25⁺ T regulatorycells, induced by peptide presented by class II MHC molecules in thepresence of IL-2, may inhibit induction of effector CD4⁺ or CD8⁺ T cellsby a contact-dependent mechanism, possibly involving cell surface and/orsecreted TGF-β, and FIG. 5D(B) illustrates how CD4⁺ NKT cells may beinduced by tumor glycolipid presented by CD1d to secrete IL-13, whichstimulates Gr-1⁺CD11b⁺ myeloid cells to produce TGF-β, which inhibitsinduction of CD8⁺ CTLs mediating tumor immunosurveillance. TGF-β mayalso inhibit CD4⁺ T cells (not shown). Blockade of other mechanisms canimprove immunosurveillance and the response to vaccines. Othersuppressor or negative regulatory cells have been described in othercontexts, but not as well study in the context of cancer (Berzofsky, etal.). Such mechanisms may have evolved to reduce inflammation andimmunopathology or to prevent autoimmunity. Tumors have co-opted thesemechanisms to evade immunosurveillance.

Thus, it has been postulated that the excess of dysfunctional CD8 Tcells is indirectly immunosuppresive by filling the “immunologic space”and shrinking the T-cell repertoire for new antigens, as well asdirectly suppressive via cytokine secretion. It is associated with theIRP predicting two and four year mortality in longitudinal studies ofvery old people. It is hypothesized that deletion of such accumulationsof dysfunctional cells would be beneficial to the individual. It may bepossible to distinguish functional CMV-specific cells (which areessential to maintain immunosurveillance) from dysfunctional ones bytheir expression of certain surface molecules. This, coupled withmethods directed at reinvigorating the thymus (such as, for example, theuse of interleukin 7), and targeting CMV by pharmacologic andimmunotherapeutic interventions might result in the immunorejuvenationsufficient to take elderly individuals out of the risk category andthereby extend healthy longevity (Pawelec, et al. 2006). Animal modelssuggest that IL-7 improves immune reconstitution through increasingthymic output and, perhaps more importantly, through antigen-independenthomeostatic driven proliferation in the periphery (Sasson, et al. 2006).A study in old Rhesus macaques showed that treatment of the elderly withIL-7 may provide an effective therapy to improve the immune system(Aspinall, et al. 2007).

In rural Gambians, the season of birth strongly predicts adultmortality. Those born during the harvest season have longer life spansthan do those born during the hungry season, and the deaths associatedwith infectious diseases suggest permanent early-life influences onimmunity (Ngom, et al. 2004). One group studied thymic size and outputin Gambian infants born in either the hungry or the harvest season bymeasuring signal-joint T cell receptor-rearrangement circles (sjTRECs)at birth and at 8 weeks of age. They found that by 8 weeks of age, thoseborn in the hungry season had significantly lower sjTREC counts(indicating poor immune function) than did those born in the harvestseason. These results correlated directly with lower ELISA measurementsof IL-7 in mothers' breast milk (Ngom, et al. 2004). This research groupspeculated that these data show a plausible pathway linking externalseason insults to mothers with thymic development in their infants,which suggests possible implications for long-term programming ofimmunity.

ImmunoScore Measurements and Applications. Thus, there is a balancebetween viral mechanisms of pathogenesis, persistence, and immuneevasion and the host cellular immune response. The immunologic basis ofthis balance has not been completely characterized. The nature andthreshold of CMV-specific T cell responses required for long-term CMVcontainment yet remain to be defined. This information would facilitateidentification of highly susceptible individuals and provide a specifictarget for immunotherapeutic approaches designed to establish, maintain,or restore immunologic protection (Sylwester, et al. 2005). There seemto be clinical consequences to an overly robust CMV-specific T cellresponse. An obvious prerequisite for a better understanding of whatconstitutes insufficient or excessive CMV-specific T cell immunity isthe ability to evaluate the overall CMV-specific T cell response ininfected individuals. Future longitudinal studies would benefit fromcombining data on viral reactivation and primary infection withimmunological monitoring (Hadrup, et al. 2006). The ImmunoScorediagnostic and database systems would provide just such an opportunityfor data collection and monitoring longitudinal data collection.

Although CMV seropositivity appears to be one of the driving forces forinduction of CD8 T cell clonality, this is not currently detectable inthe middle-age population (Hadrup, et al. 2006). The influence of CMV onclonality only becomes relevant at a detectable level in the elderly.Superior detection capabilities available through the ImmunoScoretechnology might lead to earlier detection of possible immune depletionas individuals pass through middle age.

ImmunoScore technology by its nature of compiling individual patientdata would offer the opportunity for longitudinal design of researchstudies. The longitudinal design is a superior alternative to thecross-sectional method for conducting ageing research, but it has seldombeen used due to extensive costs as studies are currently conducted. TheImmunoScore system would naturally build a longitudinal component intopatient care at no increased initial cost. The database would yieldimportant insights into ageing and all its implications at a lower costand dramatically improve healthcare.

Questions have been raised concerning CMV infection and its relationshipto the IRP (Vasto, et al. 2007). Uncertainties that requireclarification are: Is there an immunogenetic component influencing theIRP phenotype that might explain the different degree of CMV clonalexpansion vs. non-IRP phenotype? May this difference depend on socialand/or environmental factors? Might the genetic or environmentalcomponent affect the degree of clonal expansion of CMV in IRPindividuals? What can be the main cause of death in IRP? Can IRPselection be predictive in young as well as in old individuals? Is itpossible to revert/prevent accumulation of CMV-specific cells?

These are all questions that can, in exemplary embodiments of thepresent invention, be addressed by the application of ImmunoScorediagnostic and database technologies.

Immunogenetic components can, for example, be monitored using uniquetechnology designed to investigate single nucleotide polymorphisms(SNPs) rapidly and those data could be stored in the ImmunoScore centraldatabase. Additionally, social and environmental factors can be part ofthe ImmunoScore demographic data collected at routine patient visits totheir, physicians. The accumulation of these data on the ImmunoScoredatabase would yield potential relationships regarding environmental andsocial factors to the IRP.

Careful monitoring of the ImmunoScore database would shed more lightonto environmental and/or genetic factors contributing to the clonalexpansion of CMV T cells in IRP individuals and the non-IRP individuals.

As the ImmunoScore data collection system is envisaged as acradle-to-grave system of healthcare, the cause of death in IRPindividuals can be collected and collated. Preliminary indications arethat IRP selection is likely to be predictive in the young as well as inthe very elderly. The ImmunoScore cradle-to-grave philosophy of patientdata tracking can be invaluable in assessing these issues. Additionally,prevention/reversion of the accumulation of CMV-specific T cells wouldseem an issue of paramount importance. Preliminary studies in animalmodels regarding judicious use of IL-7 have been promising. ImmunoScorecan, for example, track treatments and even shed light on when suchtreatments should commence in the life of the afflicted individuals.

CMV Vaccine and Vaccines Against Chronic Viral Infections and Cancer. Ina recent review of priorities for vaccine development, CMV was ranked inthe highest of five tiers by the Institute of Medicine in the UnitedStates as a potentially cost-saving vaccine target (Stratton, et al.2000). In general, CMV is acquired earlier in life in developingcountries and among the lower socioeconomic strata of the developedcountries (Stagno and Cloud, 1990). Recently, the seroepidemiology ofCMV was examined in Australia (Seale, et al. 2006). The pattern ofage-specific seroprevalence of CMV antibody, as provided in FIG. 5C,closely matched the pattern found from analysis of the exemplary CIPdatabase described in Section II, below. Indeed, a review of CMVseroprevalence studies conducted around the world revealed thatresidents of developing countries have higher rates of CMVseropositivity than those of developed countries (Enright and Prober,2004). The potential benefits of a CMV vaccine would include reducedtransmission to pregnant women and less CMV disease due to primaryinfection or reactivation in organ transplant recipients and theimmunosuppressed (Griffiths, et al. 2000).

It is possible that the development of a vaccine that is effectiveagainst viruses that cause chronic infection may require considerationof a paradigm different than those previously used for organisms causingacute infection (Berzofsky et al. 2004). In most cases of chronic viralinfection, the immune response to the natural infection is notsufficient to eradicate that infection. The challenge for the 21^(st)century is to apply the latest fundamental knowledge in molecularbiology, virology, and immunology to developing vaccines that are moreeffective at eliciting immunity than the natural infections andconsequently, effective against chronic viral and other infectiousdiseases in addition to cancer, which do not fit the classic paradigm.ImmunoScore diagnostic and database tracking would be invaluable inanalyzing the efficacy of a CMV vaccine, as well as vaccines developedagainst HIV, hepatitis C virus (HCV), human papilloma virus (HPV) andEpstein-Barr virus (EBV), among others.

As prophylaxis against acute infectious diseases, vaccines have beenamong the most cost-effective agents, saving many millions of lives.However, for treatment of chronic infections and cancer, vaccines haveyet to achieve widespread success. Increased understanding of the immunesystem has raised new hope of harnessing the exquisite specificity ofthe immune system to attack cancer (Berzofsky, et al. 2004b). Inexemplary embodiments of the present invention exemplary ImmunoScorediagnostic panels and database systems can add considerably to thisknowledge base and can, for example, assist in intelligent vaccinedesign and monitoring of the efficacy of the vaccines as they aredeveloped.

TABLE 1 Characteristics of the Immune Risk Phenotype (IRP) CD4:CD8 ratio<1 Poor T cell proliferative responses to mitogens Increased CD8⁺CD28⁻and CD8⁺CD57⁺ cells Low B cell count CMV seropositivity Clonal expansionof CD8 cells carrying receptors for CMV High proportion of dysfunctionalcells amongst the CMV-specific CD8 cells

Table 1: Characteristics of the Immune Risk Phenotype (IRP)

CD4:CD8 ratio <1

Poor T cell proliferative responses to mitogens Increased CD8+ CD28⁻ andCD8+ CD57+ cells Low B cell count

CMV seropositivity

Clonal expansion of CD8 cells carrying receptors for CMV High proportionof dysfunctional cells amongst the CMV-specific CD8 cells

D. Exemplary Immunoscore Superpanels 1. Middle School StudentImmunoPrint Super Diagnostic Panel

In exemplary embodiments of the present invention, a middle schoolsuperpanel can, for example, comprise the following exemplary panels:

1.1. Persistent Immunity Induced by Childhood Vaccines

This panel is described above in section A3.

1.2. Sexually Transmitted Disease (STD) Diagnostic Panel

For children entering middle school (grades six through eight) abaseline determination for antibody levels to STDs is advisable.Recommended tests for ImmunoPrint measurement of immunity to STDs:

-   -   Antibodies to Chlamydia—IgG, IgA, and IgM (3)    -   Antibodies to HSV—IgG to HSV-1 and HSV-2 (2)    -   DNA analyses of HPV types—particular emphasis on high-risk    -   Antibody to N. gonorrhoeae (1)    -   Antibody to T. pallidum (1)    -   T-cell related response to T. pallidum    -   Antibody to HIV    -   T-cell related response to HIV    -   Antibodies to GBS serotypes (at least 3)    -   Measurement of Th1/Th2 cytokines (many as current evolving        definitions)    -   Antibodies to organisms that cause Urinary Tract Infection        (UTIs)        -   Escherichia coli        -   Staphylococcus saprophyticus        -   Proteus mirabilis        -   Klebsiella pneumoniae        -   Enterococcus species        -   Pseudomonas aeruginosa

Currently, there are no vaccines available for any of these STDs, withthe exception of the Merck HPV vaccine. Until this situation isameliorated as to a particular vaccine preventable disease, anImmunoScore STD diagnostic panel would thus be to recommend treatments,track immunological response or provide other analyses, and not be usedto recommend a vaccine or track the persistence of immunity conferred byit. Thus, in exemplary embodiments of the present invention an exemplaryImmunoScore database can, for example, generate correlates of protectioninformation for all disease-causing organisms. As vaccines aredeveloped, ImmunoScore diagnoses could, for example, be designed toexamine antibody and other related immune responses to vaccinecomponents.

-   -   Chlamydia trachomatis infection is the most commonly reported        sexually transmitted disease in the United States, with the        highest rates among adolescent females and young women. Because        up to 70% of chlamydial infections in women are asymptomatic,        routine screening and treatment of infected persons is essential        to prevent pelvic inflammatory disease, infertility, ectopic        pregnancy, and perinatal infections. The third U.S. Preventive        Services Task Force (USPSTF) recommends that primary care        physicians routinely screen all women whether or not they are        pregnant if they:        -   Are sexually active and aged 25 or younger.        -   Have more than one sexual partner, regardless of age.        -   Have had an STD in the past, regardless of age.        -   Do not use condoms consistently and correctly, regardless of            age.    -   According to studies reviewed by the third USPSTF:        -   The cost of screening women who are not pregnant and who are            at risk for chlamydial infection may be less than the cost            of treating Chlamydia and its complications.        -   Screening patients at greatest risk is more cost effective            than screening all patients.        -   DNA or RNA amplification tests are more sensitive than            culture.

A low cost diagnostic test for Chlamydia infection or immune response toa Chlamydia vaccine would be a welcome addition to immune statusdetermination by ImmunoPrint diagnostic testing.

-   -   Herpes simplex virus type 2 (HSV-2) is the primary cause of        genital herpes, a common sexually transmitted disease with at        least 40 to 60 million infected individuals in the U.S.        Medically serious complications of HSV are rare but constitute a        significant burden, given the high rates of HSV seropositivity        in the population. Many prophylactic and therapeutic vaccination        approaches have been explored for the prevention or treatment of        HSV infection. Infection induces both humoral and T-cell        immunity. Vaccine candidates for HSV-2 infection include subunit        vaccines, killed and live attenuated virus vaccines, and viral        DNA vaccines.    -   Human papillomaviruses (HPV) are small double-stranded DNA        viruses that are responsible for pathological conditions ranging        from benign skin warts to invasive cervical carcinomas. Cervical        cancer is the second leading cause of cancer death among women        worldwide, and more than 99% of cervical cancers contain HPV,        particularly the high-risk HRP type 16 (HPV-16). Two HPV        oncoproteins, E6 and E7, are consistently expressed in        HPV-associated cancer cells and are responsible for their        malignant transformation. These oncogenic proteins represent        ideal target antigens for developing vaccines and        immunotherapeutic strategies against HPV-associated neoplasms.        More than 10,000 American women a year are diagnosed with cancer        or precancerous cells caused by HPV, and 3,700 of them will die.        Eighty times that number will die worldwide. An effective        vaccine could prevent nearly all of those deaths. The CDC is        currently considering an HPV vaccine for all children aged 12        years. A positive recommendation by the ACIP could start states        thinking of requiring the vaccine for entry into middle school.    -   Neisseria gonorrhoeae, the causative agent or gonorrhea, is one        of the most common sexually transmitted pathogens worldwide.        Although a robust inflammatory response ensues during        symptomatic infection, no apparent protective immunity is        developed following infection, as shown in a male human        challenge study and by the high incidence of recidivism among        patients attending sexually transmitted disease clinics. The        search for a vaccine against gonorrhea has been largely        disappointing. In human vaccine trials, partially lysed        gonococci, purified pilin, and purified porin were shown to be        immunogenic, but all failed to elicit protection upon subsequent        natural exposure. The lack of protective immunity is likely due,        in part, to the capacity of many gonococcal surface antigens to        undergo high-frequency phase and antigenic variation.    -   Individuals infected with Treponema pallidum subsp. pallidum        develop specific immune responses that are able to clear        millions of treponemes from sites of primary and secondary        syphilis. Despite the fact that humans develop robust immune        responses against T. pallidum, they can be infected multiple        times. The response is a T-cell mediated delayed-type        hypersensitivity response in which T cells infiltrate syphilitic        lesions and activate macrophages to phagocytose        antibody-opsonized treponemes. How treponemes from heterologous        isolates can evade the recall response of a previously infected        individual is unknown. Data from animal studies suggest that        both antibodies and T cells play a role in protection but        neither alone prevents infection. It is possible that antigenic        diversity of T. pallidum accounts for the lack of heterologous        protection. The T. pallidum repeat protein K (TprK) is a strong        candidate for a treponemal factor involved in immune evasion.        Epitope mapping studies revealed that, during experimental        infection, T cells are directed to the conserved regions of        TprK, while the antibodies are directed to the variable regions.    -   A safe, effective prophylactic human immunodeficiency virus        (HIV) vaccine is urgently needed to curb the current AIDS        epidemic. There are currently 40 million individuals in the        world infected with HIV, and nearly 16,000 new infections occur        worldwide each day. Effective HIV-1 vaccines must be capable of        protecting immunized individuals from infection with a broad        array of diverse viral variants. Attempts to develop a safe and        effective AIDS vaccine have been slowed, in part, by the        difficulty in clearly defining specific immune responses that        can prevent infection and limit disease progression. This is in        part due to the poor immunogenicity of the envelope        glycoprotein, the tremendous variability of the virus, its        ability to evade and impair the host's immune system, and its        ability to persist by integrating into the host's immune system,        and its ability to persist by integrating into the host's genome        of a number of different cell types. It is generally believed        that an effective HIV-1 vaccine must be capable of inducing        neutralizing antibodies as well as strong cell-mediated immune        responses in outbred populations.    -   Group B Streptococci (GBS) emerged dramatically in the 1970s as        the leading cause of neonatal infection and as an important        cause of maternal uterine infection. The burden from GBS disease        in elderly persons has also increased. In 1996, the first        national consensus guidelines were released. Since then, there        has been a 70% reduction in early-onset neonatal, GBS infection.        In 2002, new national guidelines were released recommending:        -   solely a screen-based prevention strategy        -   a new algorithm for patients with penicillin allergy        -   more specific practices in certain clinical scenarios    -   Yet clinical issues remain, including implementation of new        diagnostic techniques, management of preterm rupture of        membranes, use of alternative antibiotic approaches, improvement        of compliance, prevention of low birth weight infants, emergence        of resistant organisms, and vaccine development.    -   Urinary tract infections (UTIs) are a leading cause of morbidity        and mortality and health care expenditures in persons of all        ages. Sexually active young women are disproportionately        affected, but several other populations, including elderly        persons and those undergoing genitourinary instrumentation and        catheterization, are also at risk. UTIs are the leading cause of        gram-negative bacteremia (Orenstein and Wong, 1999).    -   Lymphocytes are the effector cells of acquired immunity. Two T        helper subsets are Th1 and Th2, based on two distinct cytokine        profiles that resulted in the overall regulation of the immune        response. The Th1 cell (with its associated cytokines: INF-γ,        TNF-α, IL-2, IL-12) is biased towards the cell-mediated side of        immunity, effective against intracellular parasites, and its        down regulation of Th2 can provide relief from allergic        reactions due to IgE; but detrimental effects may result in        autoimmunity and graft rejection. On the other hand, the Th2        cell (with its associated cytokines IL-4, IL-5, IL-6, IL-10,        IL-13) favors humoral immunity, providing an effective correlate        of protection for most vaccines, and its down regulation of Th1        can result in some benefit of tolerance to prevent cellular        autoimmune reactions; but certain harmful characteristics        related to IgE-based allergies and autoimmunity may result. In        order to diagnose or predict an immunologic disease and/or        provide therapy or prophylaxis, the Th polarization status must        be determined; this should also be applied to measure        susceptibility to infectious and neoplastic diseases. Th status        is measurable in terms of cytokine profiles,        chemokine/chemoattractant receptors, specific effector cell        products, or gene expression profiles. An exemplary diagnostic        panel is described in the table below:

Th1 Th2 Cytokines Receptors Cytokines Receptors INF-γ CCR5 IL-4 CCR3TNF-α CXCR3 IL-5 CCR4 IL-2 CCR1 IL-6 CCR8 IL-12 IL-10 CRTh2 IL-13

2. Exemplary ImmunoScore Diagnostic Panels for Women of Child-BearingYears

Adult immunization rates have fallen short of national goals partlybecause of misconceptions about the safety and benefits of currentvaccines. The danger of misconceptions is magnified during pregnancywhen concerned physicians are hesitant to administer vaccines andpatients are reluctant to receive them. Routine vaccines that aregenerally safe to administer during pregnancy include diphtheria,tetanus, influenza, and hepatitis B. Other vaccines, such asmeningococcal and rabies, may be considered. Vaccines that arecontraindicated, because of the theoretical risk of fetal transmission,include measles, mumps and rubella; varicella; and BCG. A number ofother vaccines have not yet been adequately studied; therefore,theoretic risks of vaccination must be weighed against the risks ofdisease to mother and fetus.

The administration of vaccines during pregnancy poses a number ofconcerns to physicians and patients about the risk of transmitting avirus to a developing fetus. This risk is primarily theoretical. Noevidence exists of risk from vaccinating pregnant women with inactivatedvirus or bacterial vaccines or toxoids (CDC, 2002). Physicians shouldconsider vaccinating pregnant women on the basis of the risks ofvaccination versus the benefits of protection in each particularsituation, regardless of whether live or inactivated vaccines are used(Sur, et al. 2003). Generally, live-virus vaccines are contraindicatedfor pregnant women because of the theoretical risk of transmission ofvaccine virus to the fetus. The following table summarizesrecommendations for vaccines commonly administered and their indicationfor use during pregnancy.

TABLE 11 Immunizations During Pregnancy Contraindicated duringConsidered safe if pregnancy or safety Special recommendations otherwiseindicated not established pertain Tetanus and diphtheria BCG* Anthraxtoxoids (Td) Hepatitis B Measles* Hepatitis A Influenza Mumps* Japaneseencephalitis Meningococcal Rubella* Pneumococcal Rabies Varicella* Polio(IPV) Typhoid Vaccinia* Yellow fever* *= Live, attenuated vaccine

Women in their second and third trimesters of pregnancy have anincreased risk of influenza-related complications including pneumoniaand a four-fold risk of hospitalization (Neuzil, et al. 1998). The CDChas recommended that women who will be in the second or third trimesterduring influenza season and all pregnant women with additional high-riskmedical conditions should receive vaccination in the fall. Despitepublication of these guidelines, rates of vaccination among high-riskpatients remain low (Silverman and Greif, 2001; Schrag, et al. 2003).Many possible explanations exist for this discrepancy, including vaccineunavailability, logistical concerns, poor reimbursement, fear of sideeffects, and lack of adequate patient or physician education (Wallis, etal. 2004).

A number of maternal conditions were perceived as potentialcontraindications to influenza vaccination during pregnancy. The mostcommon of these were the first trimester, history of preterm labor,history of intrauterine fetal demise, and pregnancy inducedhypertension; none of these are listed by the CDC as contraindications(Wallis, et al. 2004). According to this group, another potentiallysignificant obstacle to influenza vaccination during pregnancy wasphysician reimbursement. Several responders remarked that reimbursementfrom insurance companies played a part in whether they stocked thevaccine in their offices and whether it was administered to pregnantpatients. Although they acknowledged the indications for the vaccine,some obstetricians stated that insurance plans have refusedreimbursement for vaccination because they were not the patient'sprimary care provider for this “preventive” service. Although patientsmay still be instructed to obtain vaccination elsewhere, this additionalobstacle to recommended obstetrical care may result in lowerimmunization rates. These authors concluded by stating that furtherresearch is needed to determine effective methods of increasingvaccination rates in this high-risk population.

Cytomegalovirus (CMV) is found universally throughout all geographiclocations and socioeconomic groups, and infects between 50-80% of adultsin the United States by 40 years of age. CMV is also the virus mostfrequently transmitted to a developing child before birth. The incidenceof primary CMV infection in pregnant women in the U.S. varies from 1-3%.Healthy pregnant women are not at special risk for disease from CMVinfection. When infected with CMV, most women have no symptoms and veryfew have a disease resembling mononucleosis. It is their unborn babiesthat may be at risk for congenital CMV disease. CMV remains the mostimportant cause of congenital viral infection in the U.S. For infantswho are infected by their mothers before birth, two potential problemsexist:

-   -   1. Generalized infection may occur in the infant, and symptoms        may range from moderate enlargement of the liver and spleen        (with jaundice) to fatal illness. With supportive treatment most        infants with CMV disease usually survive. However, from 80-90%        will have complications within the first few years of life that        may include hearing loss, vision impairment, and varying degrees        of mental retardation.    -   2. Another 5-10% of infants who are infected but without        symptoms at birth will subsequently have varying degrees of        hearing and mental or coordination problems.

However, these risks appear to be almost exclusively associated withwomen who previously have not been infected with CMV and who are havingtheir first infection during pregnancy. There appears to be little riskof CMV-related complications for women who have been infected at leastsix months prior to conception. The current recommendations from the CDCfor pregnant women with regard to CMV infection are:

-   -   1. Throughout the pregnancy, practice good personal hygiene,        especially hand washing with soap and water, after contact with        diapers or oral secretions (particularly with a child who is in        day care).    -   2. Women who develop a mononucleosis-like illness during        pregnancy should be evaluated for CMV infection and counseled        about the possible risks to the unborn child.    -   3. Laboratory testing for antibody to CMV can be performed to        determine if a woman already had a CMV infection.    -   4. Recovery of CMV from the cervix or urine of women at or        before the time of delivery does not warrant a cesarean section.    -   5. The demonstrated benefits of breast-feeding outweigh the        minimal risk of acquiring CMV infection from the breast-feeding        mother.    -   6. There is no need to either screen for CMV or exclude        CMV-excreting children from schools or institutions because the        virus is frequently found in many healthy children and adults.

Recently, it was found that hyperimmune globulin therapy in pregnantwomen was associated with a significantly lower risk of congenital CMVdisease (Nigro, et al. 2005). This group concluded that treatment ofpregnant women with CMV-specific hyperimmune globulin is sage, and theirfindings suggested that it may be effective in the treatment andprevention of congenital CMV infection.

Specific ImmunoScore diagnostic panel recommendations must take intoaccount the woman of child-bearing years status with regard topregnancy. Ideally, an ImmunoScore screening of a young women prior tochild-bearing years would give an appropriate “baseline” reading of thatindividual. In this instance, for example, a positive serologic test forCMV would be an indication that CMV-like illness during pregnancy wouldnot be a cause of concern regarding transmission to that mother's infantduring a pregnancy later in that woman's life.

Clearly, women of child-bearing years that are not pregnant, or notplanning to get pregnant in the six months following ImmunoScorescreening would have different recommendations than pregnant women. Anideal location and time for ImmunoScore diagnostic screening women ofchild-bearing years would be during their annual recommended visit tothe OB/GYN. An early baseline could be achieved for each patient and theSpecialist could make use of the specific recommendations withoutconfusion as to which immunizations would be appropriate. It is veryimportant to assure immunity to the components of themeasles-mumps-rubella vaccine prior to pregnancy and the ImmunoScoreservice would enable that assurance.

Accordingly, in exemplary embodiments of the present invention a Womenof Child-Bearing Years ImmunoScore superpanel can be defined as follows.

2.1. Recommended Tests for Immunoscore Measurement of Immunity:

-   -   Antibody to Cytomegalovirus (1)        -   History of CMV infection needs to be captured to complete            ImmunoScore database and add relevance to pregnancy.    -   Pregnancy test (1)

A pregnancy test is critical to making the correct decisions regardingadministration of vaccines to women of this age group. There are, ofcourse, other considerations here, but the status of the woman inquestion regarding pregnancy must be resolved in order to make accuratetherapeutic decisions. In addition to CMV antibody, the physician(s) ofwomen of child bearing years need to be aware of the recommendations ofthe CDC regarding immunizing pregnant women and the risks ofimmunization vs. the risks of foregoing immunizations. In addition,physicians should be aware that following appropriate immunizationprotocols and assuring a competent immune status is extremely importantfor women of child-bearing years.

2.2. Persistent Immunity Induced by Childhood Vaccines Diagnostic Panel

Described above.

2.3. Sexually Transmitted Disease (STD) Diagnostic Panel

Described above.

Exemplary Immunoscore System Databases A. General Overview

In exemplary embodiments of the present invention the results ofimmunologic and other assays of an individual together with additionalmedical, lifestyle, environmental and other demographic information canbe collected at the same time as, or derived from, the collected data,and can, for example, be stored in a system database. Such a databasecan, for example, serve as an electronic record of immune status andother data over a period of time, both for individuals as well as forpopulations or sub-populations, as described below. Additionally, forexample, such a database can be augmented with information regardingdiagnoses received, treatments administered, pharmaceuticals prescribed,costs of medical services performed, insurance re-imbursements, metricsas to the efficacy of treatments and/or pharmaceuticals administered, aswell other relevant information to facilitate evaluation of the efficacyand efficiency of medical services rendered, as described more fullybelow.

Thus, for example, for each run of an exemplary ImmunoScore assay withinan exemplary system, various categories of data can be collected. Datacan, for example, be stored in an electronic database using standardtechniques as are known in the art. An example of data which can bestored and the manner in which it can be stored is next described. It isunderstood that this example is not intended to preclude the storage ofadditional collected or derived data as may prove useful for thepurposes of trending, data mining, evaluation or diagnostic improvement,as described below, or as may be needed in or useful to any of theexemplary applications described in Section III below.

For each assay an exemplary system can record a unique assay ID, whichcan incorporate, for example, among other information, an identifier forthe assay instrument. This ID can be unique over the universe ofinstruments, ensuring that when data is aggregated into a central systemno two assay result records will have the same identifier. A possibleimplementation of this functionality is given, for example, byMicrosoft's use of the GUID (Globally Unique Identifier), a 16 byteidentifier generated by a computer and guaranteed to be unique acrossall computers.

Each record can include the time and date that the assay was performed,stored to a time resolution of, for example, one second. As is known,there are a variety of standard means of storing time and dateinformation in a database. One simple means is, for example, to recordthe number of seconds from an arbitrary start time, such as, forexample, Jan. 1, 1900 at midnight.

Each record can, for example, also include an indication of the locationwhere the sample was processed. This can include, for example, anidentifier of the instrument used, as well as real-world locationinformation, such as, for example, the name and address of the facilitywhere the instrument has been installed.

The aforementioned exemplary fields comprise identification informationwhich is important to maintain for all samples. In addition, informationabout the sample and patient can be stored in the database as well.Patient information can, for example, be stored in a form which isseparate from the bulk of the data, and referenced by a data link.Patient information, which can include, for example, name, socialsecurity number, birth date or other information (such as is describedbelow in detail), can be maintained with emphasis on security standardsare known in the art. The storage of identifiable individual patientinformation in a separate virtual location from the remaining data canhelp to maintain such a high level of security.

In exemplary embodiments of the present invention, a system can, foreach assay result, also store an identifier indicating exactly whichassay was performed on the sample. This can indicate not only theanalyte to be determined, but also information regarding the productionof the reagents used in the assay. This information can be used todistinguish between, and compensate for, for example, lot-to-lotvariations in assay manufacture. It can also allow for convertingdifferent assays for the same analyte into a normalized value, so thattrends across geography as well as time can be obtained.

The measurement of an immune response to a particular disease or otheranalyte can involve the collection of a large quantity of low level datagenerated by an instrument.

For an ECL instrument, for example, an instrument can measure the lightemitted from the electrochemiluminescence over some time period as wellas other information such as voltages and currents used to induce theelectrochemiluminescence and the temperature near the electrodes throughwhich the electrical energy is delivered to drive theelectrochemiluminescent reaction. From this “raw data” and possiblyinstrument calibration information, a single number, for example, can becomputed to represent an ECL signal for that measurement. Additionalinformation can be computed from the raw data and instrument calibrationinformation that indicates the quality of the ECL signal, for example,whether the instrument was operating in an appropriate environmentalcondition, whether sample was present, or whether the instrument wasoperating as expected. The raw data and such derived data can, forexample, be stored in an exemplary ImmunoScore system database. Ingeneral the size of the storage required for this raw data can varydepending upon the resolution at which the data is captured. It ispossible that a finer-grained resolution, resulting in a larger datastorage requirement, will yield more useful analysis for some assaysrather than others. Storage of both the raw data and the derived valuescan be done, for example, using industry-standard methods for thepersistence of floating point numbers. For example, four (4) bytes ofstorage, yielding approximately six (6) significant digits, can be usedfor each stored value.

The quantity of greatest interest in an assay is the concentration ofthe analyte under evaluation. This concentration can be determined byconverting a computed ECL signal to a concentration. This conversion canbe done, for example, by backfitting the ECL signal through acalibration curve that relates ECL signal to analyte concentration. Ingeneral, such a calibration curve can vary from assay to assay, and canchange over time for a given assay as that assay is refined.

Calibration curves enable both interpolation and extrapolation of ECLsignal measurements for samples with known analyte concentrations forECL signal measurements of samples of unknown amounts of analyte. Theform of the mathematical functions used in a curve fit can, for example,make assumptions regarding the continuity and/or smoothness of theunderlying relation such as through interpolating the measurements withfunctions such as piecewise constant, piecewise linear, cubic spline, orfor example, by throughfitting all the data with linear, quadratic,cubic, or quartic polynomials. For overconstrained systems, parameterscan be computed by minimizing an error function such as, for example,least squares (e.g., Press et al. 1992) or total least squares (e.g.,Van Huffel et al. 1991). The form of the mathematical function may makeassumptions about the assay mechanism, such as a one site saturation,two site saturation, one site saturation with nonspecific binding, twosite saturations with nonspecific binding, a sigmoidal dose responsecurve with or without a variable slope, one-site competition, two-sitecompetition, or a four-parameter logistic. Generation of a calibrationcurve entails selecting the form of the mathematical function and thenfitting the parameters of the function with measurements. Themeasurements can, for example, be done on the test instrument or can bedone in whole or in part elsewhere (e.g., at the place the assay ismanufactured). The measurements can either perfectly constrain orover-constrain the mathematical function. As noted, for overconstrainedsystems, model parameters can be computed by minimizing an errorfunction such as least squares.

In exemplary embodiments of the present invention, for each analyte theform of the mathematical function or model (stored, for example, as anindex into a table of known models), the computed model parameters, aswell as the data used to compute the model parameters, can be associatedwith each measurement of the analyte. To reduce the amount of redundantinformation stored in the database, the association for each measurementcan be a link to the calibration data rather than the calibration dataitself. Instruments can be re-calibrated at any time, such as, forexample, on a weekly basis or with every measurement. The quality of thecalibration can also be assessed, for example, through the running ofcontrols or by computing the residual error from an overconstrainedcurve fit.

Thus, a calculated concentration can be stored by the system. This canbe, in exemplary embodiments of the present invention, the primary inputto analysis recommendation algorithms employed by the remainder of thesystem. It is noted that not all assays will result in a quantitativeconcentration. For example, some assays, due to the shape of theircalibration curve, may yield two different concentrations for the samemeasured signal. Such assays are said to “hook.” In such cases the mostan exemplary system can store is an indicator that the measuredconcentration is above a certain level, the lower of the two returnedcalculated values. Other assays, for various reasons, may return onlyqualitative results rather than true quantitative results. In all cases,a system database can be capable of storing and retrieving the result.For this reason, in exemplary embodiments of the present invention, theresult of an assay can be stored not as a simple floating point number,but as a complex object which can take into account the variousscenarios described above. Such an object can have, for example, severalfields of its own.

A compressed version of the database can, in exemplary embodiments ofthe present invention, consist of only the initial ID information,patient ID information, test ID information, and the calculatedconcentration of analyte. This is a minimal set of data which can proveproductive for data mining and trending analysis, as detailed below. Theadditional data described herein can, for example, be used to enhancethe value of this analysis.

Algorithms encoded or implemented or implemented in an exemplary systemcan be used, for example, to determine a recommendation for action. Thisrecommendation can be based upon a calculated concentration of, forexample, antibody response. Other information can also be considered,including, for example, the results of other assays upon the same samplewithin a given assay panel.

Regardless of the means of determining the recommended action, asdescribed above, a final recommendation can be stored in the database. Asystem database can, for example, also store the “reasoning” behind therecommendation, allowing a human to later query the database todetermine why a given course of action was recommended. Given that thenumber of recommended courses of action can be broad, these actions canbe categorized and encoded. For example, a recommendation to administera particular vaccination may be encoded with one byte to indicate “givevaccination” and two additional bytes to indicate the particularvaccination that is warranted. A field for comments can also beincluded, to allow the capture of the system's reasoning—in this case,an explanation of how algorithms and rules were applied to determine thestated conclusion.

A system database according to an exemplary embodiment of the presentinvention can be implemented, for example, as a shared resource spreadover multiple computer platforms. For purposes of trending and analysis,it may be necessary to accumulate the data from a large number ofsystems into a central repository as depicted in FIG. 2, or, forexample, in the case of having only decentralized information, by usinga mechanism or process to locate and query the distributed sources. Theindividual databases can therefore require the capability to link upwith a defined central database and upload their contents. This canoccur on a periodic basis, or as may be triggered by a user of thesystem. Additionally, there can be multiple central servers, so that agiven enterprise may choose to aggregate their data at any level. UniqueIDs associated with sample and panel records can serve to allow for thecombination of data from disparate sources without data “collision.”

The linkage between local databases and a central database can beimplemented, for example, across a local area network (LAN), a privatedata network, a VPN, an intranet or across the Internet. It is alsopossible to link databases on a periodic basis using physical media,such as CD-ROMs. Similarly, various users such as, for example, healthcare providers, individuals, insurance executives, consumers of researchservices, health care management personnel, etc., can access anexemplary system via a web based interface across a local area network(LAN), a private data network, a VPN, an intranet or across theInternet.

Once data has been accumulated into a central repository, a separatesystem can be used to perform data mining and data trending analysisupon the stored data. There are many valuable sorts of analyses whichcan be performed on the accumulated data in an exemplary systemaccording to the present invention.

Given that each data record can, for example, be identified with aparticular individual or patient and a particular time and date, itbecomes possible to perform trending analysis of a patient's (or apopulation's) ImmunoScore profile over time. In many cases anindividual's absolute measured value of an analyte is not as importantas the trending of that value over a time. Some individuals may havenaturally low or naturally high values which are not best measuredagainst a statistical mean for their demographic population, but ratheragainst that individual's own measured history.

As described above, each patient can, for example, also be placed withincertain demographic categories. It can be useful to compare a patient'smeasured ImmunoScore profile against the corresponding profile for thedemographic groups to which he or she belongs. Deviation from themeasured means for a demographic slice of the population can prove moremeaningful than can a comparison to a total threshold. Thus, inexemplary embodiments of the present invention, collected data can beused to continually modify the demographic profile averages known to thesystem, taking care to not pollute the system with outlying data points.For example, it may prove useful to produce separate ImmunoScoredemographic profiles for patients who are known to have experiencedvaccinations versus those for whom there is no known immunizationrecord. Alternatively, as is described below in Section III, such animmunization record can be inferred and reconstructed, as in theprovision of ImmunoScore services to national immigration services orauthorities or bodies dealing with such concerns.

Trending information in a demographic profile, for example, can also beuseful. For example, tracking an indication of a typical person (e.g.,mean, median, or mode), or an indication of the spread amongst people(e.g., standard deviation, interquartile range, or range) over time canenable an exemplary system to assess the relationship between immunestatus indicia and external factors, such as, for example, seasonaleffects. Eating habits, sleeping habits, time aboard ship, etc. can befound to affect immune status in groups where these external factors arepartially controllable (such as, for example, in military personnel).Comparing immune status indicators of differing demographic profiles canhave important epidemiological significance.

Finally, it is expected that the collection of ImmunoScore data from alarge number of individuals and/or populations can eventually lead tothe improvement of diagnostic tests, thus forming a feedback loop. Theseimproved diagnostic tests can then, for example, be deployed to fieldinstruments, resulting in more accurate measurements and diagnoses. Suchexemplary embodiments having feedback loops can be implemented, forexample, with respect to particular populations or demographic groups,such as, for example, the military, college students, immigrants or anyother group or combination thereof as described above.

B. Exemplary Illustrative Database 1. Overall Description

To illustrate the systems and methods of the present invention, adatabase system was constructed to serve as a testbed for the exerciseof the business models described below. Such an exemplary databasesystem was used to demonstrate the tools and techniques that might beused in a full scale system according to the present invention.Accordingly, a large data set was constructed using statisticaltechniques. The data was produced according to match existing knowledgeabout the distribution of immune response values among the generalpopulation.

The exemplary database system has two primary components. These twocomponents represent the algorithmically interesting sections that canbe, for example, present in a full-scale operational system according toan embodiment of the present invention. Such a full system could, forexample, contain other modules as well, along the lines of industrystandard large scale database systems. Such an exemplary system isdepicted in FIG. 5 and is next generally described.

With reference to FIG. 5, an exemplary system architecture can beconstructed. The exemplary system architecture can be, for example,divided into two sub-systems, one relatively local to “point of care” orlocations where the individuals or patients whose immune status is to beanalyzed are located. The other subsystem can be in a central locationwhere complex data mining and analysis can occur. Thus, with referenceto FIG. 5, an upper portion of the figure contains components which canbe located at the point of care and a lower portion of the figurecontains components which can be, for example, located at a systemcentral location. The point of care is divided from the central locationin the figure by a double dotted and dashed line for ease ofidentification.

With reference to the point of care sub-system, there can be one or moreInstruments 505 which are devices which can read immunologic assays.Instruments 505 yield Assay Results 506. Assay Results 506, along withDoctor's Observations 503, Patient History 502 and DemographicInformation 501 regarding the individual or patient can all be stored inLocal PatientEvent Database 510. Database 510 can be, for example, anonline transaction processing database. Because the point of caresub-system is generally directed to generating a recommendation in arelatively short time, there are two pathways to Diagnostic Module 515.Diagnostic Module 515 applies algorithmic rules to the assay results todetermine a proper course of treatment or action based on currentreadings and optionally on past history. Thus, there is a flow ofinformation from Assay Results 506 to Diagnostic Module 515.Alternatively, Diagnostic Module 515 can implement algorithms havingother inputs besides the current Assay Results 506, such as, forexample, Demographic Information 501, Patient History 502, and Doctor'sObservations 503 (understood to include any observations by any healthcare provider, or the like, in a general sense) which can be stored inLocal PatientEvent Database 510. Thus, in FIG. 5, there is an arrowlabeled “optional” running from Local PatientEvent Database 510 toDiagnostic Module 515. Regardless of which source of informationDiagnostic Module 515 draws upon, it can, for example, output thepatient action recommendation 516 as indicated.

Returning to the central location sub-system of FIG. 5, a connectionexists between Local PatientEvent Database 510 and a CentralPatientEvent Database 520. This connects the two sub-systems. It iscontemplated that at regular intervals data from Local PatientEventDatabase 510 can be uploaded to Central PatientEvent Database 520.Moreover, although the central location sub-system could be mirrored ina number of distributed central location subsystems, the point of caresub-system is contemplated to take data from numerous instruments and infact have numerous local patient event databases in those locales. Inshort, the point of care sub-system is found wherever potentialcustomers or patients are found. It is noted that there can be a myriadof such locations, given the various and sundry applications andbusiness models that exemplary embodiments of the present inventioncontemplate. Examples of such applications are described more fully inSection III, below. Therefore, there could be a great number of localpatient event databases all of which feed into Central PatientEventDatabase 520. None of these additional point of care sub-systems areshown in FIG. 5, for reasons of ease of illustration.

Returning again to Central PatientEvent Database 520, it is noted thatthis database can, for example, also be an online transaction processingdatabase or OLTP. It is contemplated that this database can, forexample, periodically load data to an online analytic processingdatabase, or OLAP in the form of PatientEvent Database 530. PatientEventDatabase 530 can be, for example, adapted to provide inputs tocomplicated algorithms dealing with data mining and pattern detection,as next described.

PatientEvent Database 530 can, for example, reside on a central serverand utilize a data warehouse approach. There can be a variety ofconnections to PatientEvent Database 530 such as, for example, a QueryModule 531, a Data Mining Module 532 and a Pattern Detection Module 533.Query Module 531 can be, for example, an interface by which a user caninteractively search for information in database 530. Query Module 531can also access Central PatientEvent Database 520 implement a variety ofoperations on the data there as well. Data Mining Module 532 can be aninterface by which a user can interactively use OLAP tools to findstrends and summaries in the stored data. Finally, Pattern DetectionModule 533 can be a program module which can be used to automaticallysearch for patterns or other “hidden” correlations between various datapoints in a database. It is contemplated that in exemplary embodimentsof the present invention Pattern Detection Module 533 can regularly sortthrough all of the stored data looking for patterns using variousalgorithms. Some of such algorithms can, for example, articulate somehunch or a correlative assumption provided by a panel of immunologicalexperts for which they do not have hard data. Pattern Detection Module533 is thus an important feature in exemplary embodiments of the presentinvention. Additional exemplary databases which Patter Detection Module533 can utilize are described below in connection with FIG. 5A.

The exemplary system depicted in FIG. 5 will next be described ingreater detail. A first module of interest is termed Diagnostic Module515. The function of this software module is to input a set of assayresults 506 obtained through measurements by instruments 505, and tomake one or more recommendations 516 based upon the analysis of assayresults 506. Diagnostic Module 515 can be designed in such a way thatadditional assay panels can be slotted into an existing system as theyare developed.

Some exemplary algorithms used to make recommendations as a function ofassay results are described in more detail below, including descriptionsboth of algorithms used in the exemplary database as well as additionalalgorithms that could be implemented in various exemplary embodiments ofthe present invention.

Diagnostic Module 515 can rest upon a Local Database 510 containingAssay Results 506 obtained from Instruments 505. These results arepertinent to an individual patient. Local Database 510 can also, forexample, contain background medical history 502 for that patient,demographic information 501 pertinent to the patient, and a summary ofother medical observations 503 made by medical professionals or personsfulfilling a similar function. Local Database 510 can also, for example,contain statistical information obtained from a larger central database,as described below.

A second exemplary module of interest is Data Mining Module 532. WhereasDiagnostic Module 515 is intended for the analysis of a particularindividual's data at a particular point in time, Data Mining module 532can, for example, look at a broader range of data collected from manyindividuals over a range, or interval, of time. Through analysis of thiscollected data a system can, for example, be used to support variousbusiness methods and other applications by deducing trends and patternswithin an immunological landscape. A particular result could be fed backinto the Diagnostic Module's algorithms, improving their effectivenessby providing additional specificity with regard to an individual'sbackground, possibly in terms of background or demographic informationsuch as, for example, gender, racial background, geographic origin,lifestyle, economic circumstances social circumstances, or age.

As can be seen from FIG. 5, while the Diagnostic Module'sfunctionalities are primarily local in nature and patient-specific, theData Mining Module's functionalities are primarily central, andsystem-wide. As noted, this structure is reflected in the division ofFIG. 5 into two zones, the “Point of Care” zone, shown at the top of thefigure, and the “Central Location” zone, shown at the bottom of thefigure.

Data Mining Module 532 depends upon the existence of a large centraldatabase containing records from a wide variety of individuals over along span of time. Thus, the local databases described above can, forexample, exist in a federated state with the central database, uploadingtheir information on a regular basis, where this information can, forexample, be integrated into the full system.

2. Impact of Data Mining

Patterns can be detected within the data in an exemplary database whichare related to demographic and other non-immunologic information suchas, for example, gender, age, ethnicity, geographic origin, employment,etc. These patterns may not be obvious until large numbers ofindividuals are assessed, using a computer that can be by nature muchmore efficient, unbiased, and precise in pattern recognition.

From such patterns, new correlates can, for example, can be established,and old correlates can be changed. For example, in immunization relatedapplications, it may be proposed, based on previous data, that a serumantibody concentration of 2 micrograms per ml should be used torepresent a threshold of protection against meningococcal disease, sothat anyone with less antibody would be recommended for immunization.Subsequent and continued analysis, however, may show that this thresholdvalue should be reduced or raised for given individuals, depending on,for example, age or ethnic background, or some other undefinedparameter. In turn, an ethnicity evaluation could lead to the discoveryof a specific biological or genetic marker. For example, the functionalactivity of Haemophilus influenzae type b (Hib) antibodies may vary withdifferent individuals, where the same antibody concentration may notpossess the same level of bacteriocidal activity due to differences inantibody avidity. For example, regarding age, Hib polysaccharides havebeen shown to be poorly immunogenic in children less than 2 years of age(Granoff D M, 1985, J Pediatr 107:330-36). Similarly, regardingethnicity it has been shown from previous studies that Eskimos andApaches are more susceptible to Hib meningitis because they possess aless effective antibody repertoire to the Hib polysaccharide capsule,based on the presence or absence of certain variable region genes usedin the production of the polysaccharide-specific antibodies.

Additionally, variations in host factors can lead to significantdifferences in the immune response to vaccines, which can also bediscerned by data mining. For example, late-stage complement deficiencymay have no impact on antibody production, but would certainly reducethe effectiveness of those antibodies in killing bacteria, therebylowering their activity. In such case, the antibody threshold forprotection may need to be raised in order to achieve the same level ofprotection in this subpopulation.

As previously described for Hib, the capacity for protective antibodyproduction is the direct result of variable region gene haplotypes. Inthis case, ethnic differences were first observed as a gross marker, butthe presence of specific genes was later determined to be responsible.In a similar but different manner, HLA haplotypes have also beencorrelated with the susceptibility to certain infections, as well as theunresponsiveness to certain vaccines. For example, certain HLA antigensappear to be correlated with chronic hepatitis B virus (HBV) infectionsand HBV vaccine nonresponsiveness. In such cases, in exemplaryembodiments, of the present invention, subpopulations can be identified,initially by ethnicity, then later by genetics, to evolve a morespecific and appropriate diagnostic outcome.

Another example of the influence of ethnicity on responsiveness totreatment is the case of NitroMed's BiDil™, which was approved by theU.S. FDA in 2005 for the treatment of heart failure in AfricanAmericans. BiDil™ is an orally administered, nitric oxide-enhancing drugthat was shown to have clearly different effects on blacks versus whitesin clinical trials, where the “differences may be related toenvironmental, social, lifestyle, or genetic factors or to interactionsamong all of these.” (seehttp://www.fda.gov/fdac/features/2005/505_BiDil.html). In exemplaryembodiments of the present invention, data mining can, for example, beused to observe and identify these kinds of effects and correlations,and then be later used to determine the specific underlying mechanisms.

Data mining can also be used, for example, to change or reversepreviously held dogma(s) concerning long-term protection fromvaccination. For example, immunity resulting from the smallpox vaccine,used extensively during the previous century, was originally thought tolast for less than a decade. Recent analyses however, have shown that“more than 90% of volunteers vaccinated 25-75 years ago still maintainsubstantial humoral or cellular immunity (or both) against vaccinia, thevirus used to vaccinate against smallpox.” (Hammarlund E et al., 2003,Nature Medicine 9:1131-37). The same study further showed that“Antiviral antibody responses remained stable between 1-75 years aftervaccination, whereas antiviral T-cell responses declined slowly, with ahalf-life of 8-15 years.” While it is not clear what level andcombination of responses is required for protection, the authorsconcluded that “the morbidity and mortality associated with anintentional smallpox outbreak would be substantially reduced because ofpre-existing immunity in a large number of previously vaccinatedindividuals.” This is exactly the type of information that could beobtained through data mining over time on large populations, ascontemplated in exemplary embodiments of the present invention.

As noted above, an exemplary system similar to that depicted in FIG. 5was built using standard software development tools and packages. Thealgorithms were encoded using the XML data description language. Theengine for executing the algorithms was built using the Java programminglanguage. An Oracle database was used for data storage and data miningquerying. Excel spreadsheets were used for data construction andanalysis. Details of the construction are given below.

3. Diagnostic Module 3.1. Overview

Diagnostic Module 515 forms the heart of an exemplary ImmunoScoredecision system. At a basic level, the diagnostic module exists toprovide relevant information and/or to suggest courses of recommendedaction (for various purposes, depending upon the application; seeSection III below) based upon an individual's immune status, as measuredby instrumentation or obtained from elsewhere, in combination with othersupporting data. There are many different ways that such a determinationcould be made. Next described are some exemplary algorithms that wereused in the example system as well as other exemplary decision supportalgorithms which could be implemented using the same techniques.

One essential function of a diagnostic module can be, for example, toassist a medical or other professional in making decisions regardingwhich actions to take with a specific individual, making use of dataregarding that person's immune status. As noted, in exemplaryembodiments of the present invention, an individual's immune status canbe determined by conducting a panel of assays, each of which assays canproduce an element of data. For purposes of the example database,information presumed to be obtainable through such assays is summarizedin FIG. 6. It should be noted that in practice some of this informationmay not yet be obtainable, although it is expected that assays could bedeveloped along the lines of existing tests in order to complete thisspectrum.

In addition to immune status information obtained from assays, adiagnostic module can make use of other information specific to thepatient being examined. This information falls into two principalcategories: demographic information, such as, for example, age andgender, and patient medical history. Most demographic information can besimply expressed in a database. Patient medical history is moreproblematic, although there are many existing healthcare databasesystems which do this adequately. The difficulty with patient medicalhistory, however, is in devising algorithms which can make use of thisqualitative data. It is expected that particular care can be taken touse algorithmic techniques which have proven adept in dealing withinconsistent or unreliable data, such as, for example, neural networks,described in greater detail below. This is due to the inherentunreliability of self-reported medical history data, along with thehistoric problems found in the transfer of medical records. If a systemwith built-in reliability checks is implemented, then it can be possibleto rely more strongly upon historical data.

Thus, the exemplary system described below can store both demographicand past medical history information for individual patients, but doesnot make use of these factors in performing diagnostic assessments orrecommendations of courses of action. However, the algorithmsimplemented can easily be extended into these realms once moreinformation becomes available.

The output of Diagnostic Module 515 can be, for example, a series ofrecommendations. A recommendation is simply defined as any discerniblebit of data which might be of interest to a medical professional, healthcare or life insurer, medical services analyst, researcher or other userof the present invention in determining a given course of action. In thecase of a patient's immune status, a common recommendation could be, forexample, to recommend a particular vaccination, to conclude whether theindividual is in an overall sense healthy, to conclude that certainpotential hypotheses need further data to be fully explored, to tag theindividual as being potentially immunosenescent, or to grant a healthinsurance credit or debit relative to a health insurance policy or HMOmembership fee. Or, for example, a recommendation not to vaccinate, toreduce the over-vaccination of the populace. A summary of some exemplarytypes recommendations that can be offered by an exemplary DiagnosticModule are provided in FIG. 7.

In exemplary embodiments of the present invention a Diagnostic Modulecan be capable of producing a set of recommendations for each analysis.For example, it might recommend that both vaccine V be administered andthat the individual be retested in three weeks to monitor his or herresponse to such vaccine. For each recommendation, an exemplaryDiagnostic Module can, for example, also provide a confidence level,which is a measure of the system's support for any given conclusion. Auser can take this confidence level into account when deciding upon acourse of action. A course of action with a low confidence level but ahigh financial cost, for example, could be delayed until additional datacould be gathered to more strongly support the course of action.

In exemplary embodiments of the present invention a Diagnostic Modulecan, for example, be constructed in a manner to allow the deployment ofmany different algorithms within its basic shell. For the exemplarysystem, an algorithmic approach based upon perceptrons was used. Thisapproach is detailed below. Additionally described are alternativealgorithmic approaches, each of which has different strengths andweaknesses. It is noted that some of these approaches are realisticallyinfeasible until such time as large-scale data collection of immunestatus informatics becomes available.

3.2. Perceptron Algorithms

A perceptron is a simple neural network, a computer sciencerepresentation based upon an analogy with the operation of humanneurons. Perceptrons were invented by Frank Rosenblatt in 1957, and havebeen used in artificial intelligence research since that time. Aperceptron is simplistic, but adequate for the computation ofalgorithmic diagnostic results within the exemplary system of theinvention. More importantly, there is a clear progression betweenperceptrons and more sophisticated artificial intelligence techniques,which may be of use in more complex embodiments of the invention.

An example of a perceptron is given in FIGS. 8 and 8A. These networksencode the decision making process for the running of a MeningococcalDiagnostic Panel, as described above. There are seventeen inputs to thealgorithm, one for each of the measurements that can be taken in anexemplary meningococcal assay panel. Five inputs are for themeningococcal serogroups, seven for the complement components, and fivefor the genetic poymorphisms. There are two output recommendations fromthis panel R1 810 (or in FIG. 8A, R2 810) and R3 840. R1/R2 is arecommendation to vaccinate an individual with a meningococcal vaccine.R3 840 is a recommendation to monitor the individual on a stricterinterval schedule than normal, because the individual may be moresusceptible to this condition than the average individual in thepopulace. FIGS. 8 and 8A depict the same perceptron, with differentvalues for the various nodes upon firing.

With reference to FIG. 8, serum IgG levels for vaccine-preventableserogroups (A, C, W-135, and Y) of Neisseria Meningitis can be assessed.As seen in the fifth input to R1, the panel also has a built-in facilityto measure and consider serogroup B, but there is no currently availablevaccine or clearly known threshold of protection for this serogroup, soit was left blank. A serum IgG level exceeding 2.0 ug/mL for all fourserogroups would be presumptive of protection in an otherwise healthyindividual, i.e., an individual (i) found not deficient in serum levelsof measured complement components, and (ii) having no deleteriousgenetic polymorphisms as indicated in the CC Test 820 and GeneticPolymorphism Test 830. There would be no immediate recommendation formeningococcal vaccination for these individuals.

The following is a description of rule execution flow for the exemplaryperceptron of FIGS. 8 and 8A.

R1—Recommend Vaccination. With reference to FIG. 8, If the CC Test 820and the Genetic Poly Test 830 show the person is normal, both of themwill fire, giving a minimal total of 2.0 at R3. Then no contribution atR1 from R3, and if any of the serogroups is deficient, R1 will be atleast =1.0 and R1 will fire. If the CC Test 820 or the Genetic Poly Test830 show that the person is not normal, R3 840 will fire, giving a basetotal of −4.0. Nothing will be contributed from the R3 conclusion aseven if the inputs to R1 810 from the four serogroup assays are all 1.0(all deficient), this added to −4.0=0, which is <1.0, and R1 needs tobe >=1.0 to fire. Thus FIG. 8 only operates as to normal individualsvis-à-vis the CC and Genetic Poly tests.

R3—Recommend Flagging. If the total at R3 840 is less than 2.0, theindividual is not normal, R3 fires and the recommendation will be toflag this individual for monitoring.

FIG. 8A is similar to FIG. 8, except that it applies a differentrecommend vaccination rule, R2 at 810, for a different immunologicalcontext. The perceptron is modified as to values, but the nodes areidentical.

R2—Recommend Vaccination. With reference to FIG. 8A, if deficiencieswere to be revealed in any of an individual's complement components, orif any unfavorable genetic polymorphisms were shown to exist, then it islikely that a serum IgG level of >5.0 ug/mL (not the >2.0 UG level as inthe rule of FIG. 8) for the vaccine-preventable serogroups would bedesirable in these individuals. If these individuals had IgG levelsexceeding 5.0 ug/mL for all four serogroups, no vaccination would berecommended. If the level of antibody to any of the four serogroups wereto be below 5.0 ug/mL, then a vaccination would be recommended. If theCC Test or the Genetic Poly Test show the person is not normal, one ofthem will fire, giving a minimal total of 10 at R2. Then, all that isrequired is for one of the serogroups to be deficient (i.e., <5.0 ug/ml)in order for the recommendation at R2 to evaluate to true.

R3—Recommend Flagging. If the CC Test and the Genetic Poly Test show theperson is normal, both of them will fire, giving a minimal total of 2.0.If the total is less than 2.0, R3 fires, they are not normal and therecommendation will be to flag this individual for monitoring.

Because all perceptrons operate on the data in parallel, an abnormalindividual can, for example, be captured in the perceptron of FIG. 8Aand can thus receive no vaccination recommendation from the perceptronof FIG. 8.

A perceptron operates through software by simulating the “firing” ofnodes based upon numerical conditions being met. As each node fires, itcan contribute to the firing of other nodes, in some cases positivelyand in some cases in an inhibitory fashion. The network as a whole hascompleted execution when the rightmost nodes, representing diagnosticrecommendations, have either fired or have come to rest.

The perceptrons in the exemplary system were encoded manually based uponexisting knowledge of diagnostic recommendations in use today. Eachperceptron can be represented either graphically, as in FIG. 8, ortextually, as in FIG. 9. FIG. 9 is thus a textual representation of theperceptron network using a language called XML, or eXtensible MarkupLanguage. In the exemplary these XML files can be deployed to thediagnostic module as discrete packets. An exemplary Diagnostic Moduleconnected to an instrument, or bank of instruments, could, for example,be configured with only those perceptron algorithms required for thatsite.

In addition, updated versions of these algorithms could be deployed asthe algorithms are improved over time in a continuous process of systemlearning or iteration. Thus, in exemplary embodiments of the presentinvention, knowledge gained through use of the data mining module,detailed below, can be fed back into the individual diagnostic modules,thus improving the accuracy of the entire system. For example, it may bededuced through data mining of an exemplary database that the level ofantibody activity which is a strong indication of the need forvaccination is lower in men than in women. A new perceptron algorithmcould then be deployed, for example, including the gender of the patientas a new input node, with a link to the vaccination recommendation node.

More subtly, a perceptron can include within it a series of weightswhich can, for example, correspond to the importance of each bit ofevidence to the recommendation procedure. Over time these weights can becontinually adjusted and redeployed to reflect increased understandingof the role of each of the immunological factors being measured.

3.3. Alternate Algorithmic Approaches

There are a number of alternate algorithmic approaches which can be usedwithin a Diagnostic Module. Each has varying strengths and weaknesses.An exemplary system can, for example, include a combination of theseapproaches in order to come up with the most complete recommendation fora course of action.

The process of evaluating algorithmic approaches involves aconsideration of the goals which are to be met. A Diagnostic Module can,for example, be configured to optimize for any one of a number ofdifferent criteria. Possible goals can include, for example, optimizingthe welfare of the patient, minimizing costs for the patient related tothe disease in question, minimizing overall patient healthcare costs,and minimizing life insurance costs. The decision algorithm used in thediagnostic module can thus vary depending on how these goals areprioritized.

A key difference between a system according to the present invention andexisting systems is the use of an individual's immune status informationand associated data as inputs to the decision procedure. This allows thesystem to provide more tailored and individualized recommendationsinstead of relying upon aggregate statistical measures. A second keydifference is the introduction of historical patient immune status andother data. It is possible, for example, that a given individual'santibody level is below some computed norm, but is in fact high inrelation to that individual's past results. This might conventionallybe, for example, a contraindication for vaccination, a recommendationwhich would not be made if the individual's immune status were only tobe compared to the population standards.

Using the exemplary symbology laid out in FIG. 10, various diagnosticgoals as shown in FIG. 11 can be summarized.

3.4. Additional Input Data

This section describes additional data which could be incorporated intoa diagnostic module in exemplary embodiments of the present invention.

As noted above, historical immune status information can be a usefuladdition. Basing a recommendation solely upon an individual's status atthe current point in time is an adequate approach, but it risks makingincorrect recommendations for those patients who do not fall within theaverage range of the population at large. A simple extension to thesystem would be to move away from absolute measures of, for example,antibody level and antibody activity level, and to substitute insteadrelative measures based upon the percent change in these values sincethe last historical measurement, or in comparison to the individual'shistorical averages. The same decision procedures could be applied, butretooled so that a decision rule such as “the level is greater than 30”becomes “the level is greater than 15% above the patient's baseline”. Inorder for this to occur, an exemplary system can either maintain acentral record of the patient's immune status over time, or providemeans to allow the portable storage and transfer of this historicalrecord, perhaps under the patient's control. Various forms of“smartcard” or electronic storage technologies as are known could beused for this purpose.

A second type of additional input data relates to demographicinformation. Current decision procedures do little to distinguishtreatment recommendations based upon an individual's age, gender orracial background, although it is known that these factors have aconsiderable effect on the interpretation of immune status information.Thus, an exemplary system could make use of such demographicinformation, customizing the diagnostic algorithms to take into accountobserved patterns. Additional research would be required to deduce thesepatterns in the population as a whole in order to make reasonablemodifications to the decision procedures.

3.5. Decision Rule Algorithms

A clear successor to the perceptron approach could be to extend thesystem to full neural networks. The distinction between perceptrons andmore complex neural networks is the incorporation into the latter offeedback links from later nodes to earlier nodes in the network. Thisnot only increases the complexity of the algorithms which can beimplemented, but allows for algorithms which improve over time through alearning mechanism. Neural networks are a well-established domain ofartificial research. The primary impediment to neural networks is thatthey are difficult to construct by hand. A typical neural network isinstead evolved through the use of training algorithms. These trainingalgorithms require as input a set of training data. In an exemplaryembodiment of the present invention, the training data could consist ofimmune status data from a large population of people coupled with dataabout the eventual onset of diseases in that population. Were such adatabase to exist, neural networks could be constructed which couldpredict the onset of disease based upon features in an individual'simmune status information. An advantage to using neural networks is thatthey could be a simple drop-in replacement to the current DiagnosticModule in terms of inputs and outputs.

4. Data Mining Module 4.1. Overview

The Data Mining Module is the large-scale component of exemplary systemsaccording to the present invention. As noted above, while the DiagnosticModule focuses upon obtaining results specific to a particularindividual, the Data Mining Module can be, for example, designed toexamine trends in large data sets assembled for many individuals andwith many readings per individual. This capability is necessary tosupport business models in which information is deduced about immunestatus patterns, as well as to improve the functionality of theDiagnostic Module over time.

As noted, an exemplary system was constructed using an Oracle databaseserver. The schema for the database system is given in FIGS. 12 through14. The schema used is termed a ‘star schema’, which is a databaselayout optimized for online analytical processing. This is a standardconcept in data mining. More information about the data storage is givenbelow.

4.2. Sample Data

A sample database was intended to represent actual immune statusinformation which could be collected from a large population over alarge span of time. The test measurements contained within the databaseare randomly generated within the constraints detailed below.

The exemplary database contains three distinct sorts of information.

The first block of information is individual immune status information.As an example, the individual is assumed to be a patient in somehealthcare context. The schema for the patient information table isgiven in FIG. 12. To summarize, the database contains information on thepatient's birthdate, gender, racial background and geographic location.All of this information can potentially be used for data mining effortsrelated to immune status. The database also contains other informationstrictly for identification purposes, such as name and ID.

In the exemplary database, patient information was randomly generated.Gender was split evenly, and geographic placement was divided among fourtest cities. Racial backgrounds were assigned to match latest U.S.census figures available.

The second block of information is patient visit information. A schemafor the patient visit information table is given in FIG. 13. Tosummarize, this information covers data that could, for example, becollected by a physician at the time of a patient's visit. There can bemultiple visit information records for each patient. The majority ofthis information covers various symptoms present in the patient at thetime of the visit. This information can be used within the DiagnosticModule, above, as part of an algorithm which takes into accountdiagnostic information other than the immune status assay results. Thisinformation can also be used in data mining to discover correlationsbetween physical symptoms, immune status indicator levels, andsubsequent onset of disease. The visit information section of thedatabase is also used to store recommendations from the DiagnosticModule.

In the exemplary database, symptomatic information was assignedrandomly. The example Diagnostic Module did not make use of symptomaticinformation.

The third block of information is the actual results of immune statusassays. In the exemplary database there are 48 distinct simulatedmeasured quantities, although this can be expanded, for example, to anyreasonable number in a straightforward manner. The schema for this datablock is given in FIG. 14.

In the exemplary database, assay test results are generated with care.The distribution of antibody levels are randomly generated based upon alog-normal distribution with an average of 50 micrograms per milliliter,as is consistent with measured antibody levels in practice. These valuesare used as initial baseline levels for the patients in the database.New values are then entered to simulate readings taken at set timeintervals in the exemplary patients' lives, as indicated in FIG. 15. Ateach age, the antibody levels were perturbed using a small normaldistribution, to simulate variation in the population over time. Resultsare biased to match the observed behavior of antibody activity inpopulations as they age, as shown in FIG. 16. All data in FIG. 16 isfrom simulated vaccinated patients.

Half of the sample population was treated as if they had received astandard vaccination schedule at age 5; the other half was leftuntreated. Antibody levels were adjusted to suit, as shown in FIG. 17.In addition, a subset of patients were given artificially loweredcomplement levels and antibody activity levels with no change to themeasured antibody levels, simulating the effect of complement-deficientpatients on the data mining procedure. This is shown in FIGS. 18 and 19.

The intent behind this production of sample data was to produce apopulation with interesting characteristics that could be highlighted inthe data mining module. Although the exact features used may not bestrictly representative of the population as a whole, they represent thetype of correlation that a system such as this could detect within realpatient data. It could easily be imagined, for example, that individualsof a particular racial background might naturally have elevated levelsof a particular antibody. The system being described could be used todeduce that fact, which may have implications for the immunological carethat such individuals would receive.

It is noted that all assay results, such as antibody levels, such as,for example, “Gcmp AVG” in FIG. 16, may be measured and quantified asunits (U) per volume (e.g., ml), where U may be defined as somearbitrary unit of a particular assay for the purpose of relativecomparisons. In addition, U may be replaced by a more precisemeasurement of mass, such as micrograms, where possible and appropriate.Antibody activity, such as, for example, “Gcmp AVG” in FIG. 16, refersto the functional activity of an antibody, which may consist of, but notnecessarily be restricted to, bactericidal or bacterial killingproperties. In these specific examples, assay results from individualsmay be processed for statistical purposes in the evaluation of apopulation, as in FIG. 16, where individuals may be averaged (AVG) byappropriate statistical formulas. Where statistical processing assumes anormal distribution, geometric means may be used to average the resultsfrom different individuals, thereby requiring a log transformation ofdata sets, since it is generally found that only the log values ofimmune responses will follow a normal distribution.

4.3. Exemplary Use of the Patent Event Database

In exemplary embodiments of the present invention, a database used fordata mining can, for example, be accessed in three different modes, asindicated in FIG. 5.

A first mode can be, for example, an interactive query mode. A user caninteractively search for results in the database. Typically queriesmight include the retrieval of a single individual's immune status overtime, or the comparison of two such individuals, as shown in FIG. 19.Queries can be submitted, for example, using either a graphical querytool or through the use of Structured Query Language (SQL), a computerlanguage for the querying of databases. An exemplary SQL query is shownin FIG. 19A. Both of these methods of access are well-known in theindustry. With reference to FIG. 5, a user can use the query mode viaQuery Module 531.

A second exemplary mode is the use of Online Analytical Processingtools, or OLAP tools, to find patterns within the database. A simpleexample of this is the production of aggregate statistics forsubpopulations within the whole. In FIG. 19B, for example, a query forcorrelation coefficients to GCMP levels is restricted to femalepatients. A similar query might look at only patients from a distinctgeographical area or racial background. Correlation statistics can alsobe generated, to test hypotheses about possible causal links amongmeasured antibodies, between antibody measurements and physicalsymptoms, or correlations between any of these and demographicinformation. The utility of such a tool depends directly on the quantityand quality of data that is input into the system. For the exemplarysystem, trends that were deliberately introduced into the sample datacan be “discovered”, but other correlations are simply a function ofrandom noise. In a real system, a variety of interesting patterns can bededuced. For the exemplary database, standard OLAP tools were used. Withreference to FIG. 5, a user can use the data mining mode via Data MiningModule 532.

A third exemplary mode that is anticipated is the construction of apattern detection module. This can, for example, comprise softwareprogrammed to sift through the accumulated immune status and other dataand search for patterns that might not be evident to a human observer.It is generally true that there are statistically significant patternsin the underlying data which are too subtle or too complex for simpledetection schemes. Such an automated detection system can, for example,rely upon one or more of the artificial intelligence pattern recognitiontechniques as described above and in the standard literature. Inexemplary embodiments of the present invention both neural networks andgenetic algorithms can, for example, be used to perform this task. Withreference to FIG. 5, a user can use the pattern detection mode viaPattern Detection Module 533.

FIG. 5A illustrates an alternative exemplary system architecture to thatof FIG. 5. FIG. 5A has a few additions, namely, Hypothesis Database 560and Rules Database 565. Each of these databases can be used, forexample, when pattern detection module 533 discovers a correlationbetween database variables. When that occurs, a list of suchcorrelations can, for example, be reported to a human expert or group ofexperts for review. Or, for example, an intelligent system can attemptto recognize the characteristics of such a correlation and associatepossible hypotheses to explain it. These can be generated, for example,from a Hypothesis Database 560, and the rules by which a givencorrelation can, for example, be mapped to one or more hypotheses can bestored, for example, in a Rules Database 565. In such exemplaryembodiments, once a set of hypotheses is generated, an exemplary systemitself can go back and mine the data to either rule out, corroborate, orconfirm that there is insufficient data to either confirm or rule out,each hypothesis in the set. In the latter case the system can recommendthat further information be collected, such as, for example, viaadditional assay panels known to the system, lab tests, additionalpatient history items, etc. This process is described in greater detailbelow.

C. Exemplary Canadian Immigrant Project Database Used to Illustrate DataMining and Hypothesis Generation

Appendix A contains selections (i.e. an initial set of records) from anexemplary database which was used to illustrate various data miningfunctionalities according to exemplary embodiments of the presentinvention. The database was created from data obtained in interviewswith and by performing tests on blood obtained from a number of newlyarrived immigrants to Canada under the auspices of Dr. Chris Greenaway(Assistant Professor in the Department of Medicine, McGill University,and a staff physician in the Departments of Microbiology and InternalMedicine, Sir Mortimer B. Davis Jewish General Hospital, Montreal,Quebec). As can be seen from the initial pages of the database, thereare entries for assay results for each of measles, mumps, varicella,rubella, hepatitis A, hepatitis B, hepatitis C, tetanus, diphtheria,cytomegalovirus, strongoloides, schistosoma, filarial, and sixteencytokines (IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70,IL-13, IL-15, IL-17, IL-23, IFN-γ, TNF-α, TNF-β), as well as otherfactors such as age, gender, region/country of origin, socioeconomicstatus, etc. In the descriptions that follow, this database willsometimes be referred to as the CIP database (for Canadian ImmigrationProject). The entire CIP database has approximately 1500 records. Thedatabase contains, specifically, the following data:

Immunological Tests: Hepatitis A

Measles (two different manufacturers for diagnostic testing)

Mumps Rubella Varicella Tetanus Diphtheria Cytomegalovirus Hepatitis BHepatitis C CMV Strongoloides Schistosoma Filaria IL-1α IL-1β IL-2 IL-4IL-5 IL-6 IL-8 IL-10 IL-12p70 IL-13 IL-15 IL-17 IL-23 IFN-γ TNF-α

TNF-β

Historical/Demographic Data:

Region of Origin, being one of:

Sub-Saharan Africa Latin America and South America Caribbean EuropeEastern Europe South Asia Southeast Asia North Africa/Middle East

Demographic information:Date recruited

Gender

Age (all participants were adults ≧18 years of age)Whether the interview was taken through an interpreterCountry of origin, being one of:

India Bangladesh Sri Lanka Pakistan Morocco Vietnam Congo Other

Date moved to CanadaCitizenship status, being one ofRefugee claimant

Refugee Immigrant Other Pregnancy

History of vaccine-preventable diseasesParticipant had written vaccination record?Participant's residence in home country had indoor toilet/no indoortoiletIf indoor toilet,

Flush? Other?

Participant's residence in home country had outdoor toilet/no outdoortoiletIf outdoor toilet,

Outhouse?

Covered pit latrine?

Other?

Participant's residence in home country water supplyTap inside?Tap outside?Closed well?Public stand pipe?

Bottle? Pump? River?

Pump earth system?Tap inside and closed well?

Other?

University education?Participant's residence in home country degree of crowding (number ofindividuals/room)?Participant's residence in home country had electricity/no electricity?

The CIP database could be augmented to facilitate a broader scope ofdata mining. In such embodiments results of the following assays couldbe added: Tuberculosis, Avian (H5N1) flu, Pandemic flu (not necessarilyH5N1), Chronic infectious diseases other than CMV

EBV, Herpes/Type?, Zoster outbreak/varicella antibody level followingoutbreak?, HPV, HIV

HTLV I, Helicobacter pylori, Lyme disease, Tularemia, Parasiteinfections, Malaria, Strongyloides, Hantavirus, Leishmaniasis,Toxoplasmosis (particularly among pregnant women), Antibody levels toother infectious diseases currently on vaccination schedules: Hib,

Pneumococcal (conjugate vs. PS vaccines); Meningococcal (conjugate vs.PS vaccines); Poliovirus; Traveler's vaccines; Japanese encephalitis;Cholera; Yellow fever; Military-specific vaccines: Anthrax, Smallpox,Plague, Rabies; Other infectious agents not currently vaccinated for,including: Staphylococcus aureus, Moraxella catarrhalis.

In exemplary embodiments of the present invention, the followingnon-immunologic data can, for example, also be obtained and stored in anindividual's exemplary Immunoscore database record:

Environmental considerationsZip/Postal codeRural/Urban home environmentWorking environmentmany interactions with many peoplefew interactions with few peopleInteractions with types of people at home/work:adultschildren/age of childreninteractions with local travelersinteractions with global travelersCommute to workpublic transport/drive?duration?crowded/stressful?Power sourceproximity to power linestype of fuelproximity to power plant

Water

sourcewellcitynature of treatment

Nutrition Diet

high/low fatmeat/vegetable intakeCommon food infections

Salmonella Cholera Hepatitis A Typhoid

Alcohol consumption/volumeVitamin supplements

Fitness

Regular exercise/sedentaryCardiac/blood pressure assessmentHistory of smokingSecond hand smokingSchool(s) attendedDay school/boarding schoolCrowding at school?Work environmenthigh/low/intermediate stressjob satisfactionoccupationwork described as physical/mental/combination?safety considerations at work?infectious organisms presentnosocomial infections a concern?chemical agents?Air qualityhomeworkAnimal exposurepetsworkfarmlableisurewooded environment?horseback riding?Family/personal history:Chronic disease/nature?

Cancer/type?

Heart disease

Diabetes

Known immunodeficiency

Asthma

Kidney diseaseLiver diseaseLung disease

Allergies/type?

Mental illnessBack problemsJoint pain/injury?Chronic fatigue

Osteoporosis Arthritis Epilepsy

Education levelhighest grade achievedEducation typepublic/private?education environmentcrowding?stressquality of school (measured objectively, of course)Military service?Nature of deploymentService branch?

Rank

It is understood that an exemplary database according to the presentinvention can contain records for various individuals from differentcountries and locales, being managed under various health care systems,and that various types of assays can be used to obtain assay results.Thus, in exemplary embodiments of the present invention, the data storedin the database can, for example, be normalized to some database widestandard defined for each data field used in the database, or, forexample, can be stored in its original form and any algorithm that seeksto access data first performs normalizing of the various records whichare input to that algorithm. It is for the purposes of such normalizingthat information regarding assay manufacturer, type, and curve that mapsan OD or other assay raw result to IUs of an antibody or other measuredbiochemical needs, in general, to be stored in the database.

Next described are the results of data mining and hypothesis generationstudies performed on the exemplary CIP database. These examplesillustrate methods and techniques that can be used in exemplaryembodiments of the present invention.

D. Data Mining—Analyses and Conclusions

In exemplary embodiments of the present invention, immunologicinformation stored in an exemplary database (such as, for example, theCIP database, described above) can, for example, be analyzed in variousways and related to other variables in the database. Three usefulexamples of such analysis can, for example, include: (1) linearregression analysis on two variables to determine whether a positive ora negative correlation exists; (2) comparison of geometric mean immunevalues (obtained, for example, as antibody concentration, opticaldensity, etc.) for both genders by geographical regions; and (3)percentage of positive or negative support within a population for onevariable with respect to another. Examples of such analyses aredescribed below using data from the CIP database.

1. Linear Regression Analysis—Correlation Coefficients

In exemplary embodiments of the present invention, tables of correlationcoefficients (r) can, for example, be generated when comparing oneparticular immunologic variable (such as, for example, varicellaantibody optical density) against other disease-related immunemeasurements, either for both genders together or separately. Forexample, FIG. 20 presents the correlation coefficients between VaricellaOD and various other variables in the CIP database. FIG. 20 presentsthree tables. The top table is the correlation of Varicella OD with eachof nine other variables from the CIP database for all persons in the CIPdatabase. The second and third tables present this informationsegregating males and females. For example, in FIG. 20, r values havebeen highlighted by shading when they are either >0.05 or ≦−0.05, as ameans of readily identifying patterns of relatedness (where |r|≧0.05 isconsidered as “related”).

With reference to FIG. 20, Varicella optical density is obviously highlypositively correlated with Varicella titration dilution, inasmuch as oneis calculated from the other, but other relationships also appear,although somewhat less pronounced. If the genders are separated, thenthe relationships appear even more strongly, as expected, since scatteris thus reduced. From the tables presented in FIG. 20, Measles and Mumpsimmunity (Dade assay) appear to be slightly correlated with Varicellaimmunity.

The r values in the tables can also, for example, be graphed in such away so as to better visualize any condition patterns, as is shown, forexample, in FIG. 20A. Again, the Measles and Mumps relationship toVaricella stands out above the others (not considering the Varicellatitration dilution data, which is obviously correlated to Varicella OD).

2. Geometric Mean Values

In exemplary embodiments of the present invention, immune data can, forexample, be statistically analyzed for the purpose of characterizingpopulations of different geographical regions, as well as for comparingresults across genders. Such mean values can thus be graphicallycompared by gender and region to visualize population dynamics. Forexample, the geometric means of Rubella antibody concentrations fordifferent regions can be graphically analyzed by gender, as shown inFIG. 20B. With reference thereto, a trend can be seen where males havehigher antibody levels than females across all populations in thedatabase. It is also apparent that persons from Southeast Asia show alower antibody level relative to the other regions in this study. Tohelp facilitate this assessment, dotted lines were drawn on FIG. 20B toindicate the mean of the means (geometric) from all of the populations(excluding Southeast Asia) separately for males and females. The arrowsabove the bars for the Southeast Asia data show the difference betweenthe mean values for Southeast Asia compared with such mean of the meansfor all other regions. It would thus appear that Southeast Asia has alower immune profile for Rubella. This can, for example, be explained asthe effect of (i) no specific Rubella vaccine program; and (ii) apossibly a lower exposure rate compared with the rest of the world,making Southeast Asians more susceptible to this disease when travelingto other geographic regions.

This finding highlights one of the many potential uses of the presentinvention. As described below in Section III, exemplary embodiments ofthe present invention can be directed to health insurance underwriting.Here, for example, knowledge of the fact that Southeast Asians tend tobe vulnerable to Rubella would indicate that such persons, as acondition of maintaining insured status under a health plan or HMO,could be required to obtain Rubella vaccination.

In a similar manner, the geometric means of Hep A units in the CIPdatabase (which are inversely proportional to antibody concentrationsand derived from immunoassays), were plotted in FIG. 20C for differentgeographical regions, again separately for each gender. In this case, itappears that there is no significant difference between males andfemales across all populations except one, Eastern Europe. Also, onceagain, Southeast Asia appears to be different from the other regions,where the Hep A antibodies are lower, as shown by higher assay unitswhich, as noted, are inversely related to antibody concentration. Inaddition, persons from Eastern Europe are also seen as being generallylower in antibodies, and the Eastern European females (dotted bars) areseen as having particularly lower antibodies than the males. Again, inFIG. 20C a dotted line has been drawn to represent the mean of the means(geometric) from all of the populations, excluding Southeast Asia andEast Europe, but combining males and females. Another dotted line hasbeen drawn to represent the mean of the means from the excludedpopulations, except for the Eastern European females, which arenoticeably higher in units (and thus lower in antibodies). The arrows inFIG. 20C highlight the differences between (i) the overall populationmean of means and the mean for Southeast Asians and Eastern Europeanmales; and (ii) the overall population mean of means and the unit levelsfor Eastern European females. Overall, there appears to be less Hep Areactivity for Southeast Asia and Eastern Europe when compared withother regions; this is especially so among East European females. Thismay, for example, indicate no vaccination and possibly less exposure,with greater disease susceptibility. Thus, from a healthinsurance/health management perspective, an adult female from EasternEurope should have a Hep A vaccination.

3. Percent Support Between Variables

In exemplary embodiments of the present invention, the percentage of apopulation that demonstrates a positive or negative relationship for onevariable with respect to another variable can, for example, bedetermined and graphically analyzed. For example, using data from theCIP database, Rubella antibody levels were measured in females fromChina, and the results were grouped according to immune status: immunesupport (protective high antibody level), low level support (equivocalantibody level), or susceptible support (non-immune antibody level). Thepercentage of each of these groups that supports an association withanother immune variable, either positively or negatively, for variousdifferent diseases was then plotted in FIG. 20D. It is apparent thatthere is no significant difference in support for Rubella with Hep A(non-reactive or reactive) or with Varicella (positive); once again, inFIG. 20D dotted lines have been drawn to help visualize that the Rubellaimmune levels show no clear trend from immune to lower immunity tosusceptible in these specific cases of other diseases. However, asregards Mumps, there is a clear trend for Rubella immune support whencompared with Mumps. The arrow shows that there is a greater percentageof Rubella immune support for positive mumps, i.e., immune response forRubella is correlated with that for Mumps.

The immune support of Mumps for other immune variables can, for example,also be used to compare different geographical regions, as is shown, forexample, in FIG. 20E. In FIG. 20E, only the positive and negative Mumpssupport groups are plotted (leaving out the equivocal “low levelsupport” group) for each of Eastern Europe and Sub Saharan Africa withrespect to Hep A=non-reactive, measles=positive, and Rubella=immune.Dotted lines have been drawn to illustrate that there is no differencein Mumps immune support for Hep A=non-reactive in both regions, but thearrows show that there is a difference for Measles=positive in EasternEurope only, and a difference for Rubella=immune in both regions. Thus,a higher percentage of Mumps immunity is seen with Measles immunity inEast Europe, and with Rubella immunity in both East Europe and SubSaharan Africa.

In exemplary embodiments of the present invention, immune support canalso be related to other variables that do not measure immune status,such as, for example, education. An example of such a correlationanalysis is shown in FIG. 20F. In this example, the positive andnegative Mumps support groups are plotted for Southeast Asia and EastEurope with respect to university attendance. From these results itappears that for Southeast Asia, a higher percentage of negative Mumpsimmune support occurs when there is less university attendance, and inthe expected reciprocal way, a higher percentage of positive Mumpsimmune support occurs with university attendance. For Eastern Europe,however, there is no relationship seen between Mumps immune support anduniversity attendance.

4. Possible Conclusions

The data mining examples described above demonstrate the usefulness, inexemplary embodiments of the present invention, of an analysis ofrelationships among different variables, both immunologic and otherwisein an unbiased mathematical manner. Regression analysis can, forexample, be performed to just look for correlations at random, but therelationships may be weak and difficult to see. Additionally, forexample, population means can be used to detect broad populationdifferences or similarities. Also, percentage support analysis betweendifferent variables can, for example, allow for a greater focus onspecific relationships between different immune status results and otherfactors that may affect them.

The examples described above point towards interesting correlations,some of which can be explained based on known immunization practices,and others which may, for example, indicate previously unforeseenrelationships involving exposure to disease. For example, in countrieswhere MMR (Measles, Mumps, Rubella) vaccines are administered, one mightexpect to see a clear correlation of immunity for all three diseases;but this would usually occur only in developed countries such as theU.S., Canada, and parts of Europe. Also, in some cases, there may onlybe single immunizations for Measles. The immigrant populations used inthe examples discussed above, however, were most likely not immunizedfor the diseases under analysis, and thus most of the observed immunitywould be due to environmental exposure to the infectious agents ofdisease, or possibly some other agents or substances that cross-reactwith these disease agents.

Due to socioeconomic conditions in these regions, it is possible thatexposure to one disease might also indicate exposure to others,particularly in crowded areas, or areas where diseases are known to beendemic. It is therefore not surprising to see positive correlationsbetween Mumps and Measles or Rubella, as seen in the China and EasternEurope data. In certain circumstances, however, the disease exposure maybe so prevalent (>90% of population) that there would be no way toestablish correlations to other factors since everyone has it; thismight be the case, for example, for Mumps support with Measles in SubSaharan Africa as shown in FIG. 20E. Increased immunity for Mumps inSoutheast Asia for those who attend a university could be the result ofthese more fortunate people being allowed greater access to vaccines,or, for example, it could be due to greater disease exposure in crowdeddormitories. No difference in Mumps for university attendance in EasternEurope might mean that there is greater disease incidence, or, forexample, that there is greater university attendance, since bothpositive and negative support percentages are high. No difference inRubella support for Hep A reactivity or positive Varicella in China maybe, for example, the result of higher disease prevalence and exposureoverall. A trend towards higher Rubella antibodies in males for allregions might indicate an unforeseen gender preference that couldwarrant further epidemiological studies in relation genetic polymorphismif this is not the result of broad cultural practices regardingvaccinations or disease exposure. The significantly lower Hep A antibodylevels (higher assay units) only for females in Eastern Europe may, forexample, might indicate a cultural phenomenon for further study.

These examples merely scratch the surface of what can be explored interms of epidemiology, immunity, socioeconomics, and geneticpolymorphism in exemplary embodiments of the present invention. Suchexemplary analyses, can be used, for example, to design more focusedstudies on specific areas of interest or, for example, to test specificrelationships that are only hinted at in the beginning. It is alsouseful to remember that the data in these examples only representimmigrants entering Canada; it may therefore be important, in exemplaryembodiments of the present invention, to collect more samples and expandthe database to other population segments, and/or to follow the samepersons through time taking samples of each participant annually for anextended period of time.

As can be seen from the above description, in exemplary embodiments ofthe present invention, once a list of correlations has been obtained byanalysis of a given set of records in an exemplary database, eitherhumans or intelligent systems are needed to postulate explanatoryhypothesis, which can then be verified, or at least can be attempted tobe verified, excluded or determined as inconclusive.

E. Pattern Detection and Hypothesis Generation

FIG. 21A illustrates an exemplary process flow for pattern detectionaccording to exemplary embodiments of the present invention. Withreference thereto, at 21A01 patient information attributes can becollected and then grouped together into separate logical groupings. Thefollowing table illustrates such an exemplary grouping.

Logical group Attributes Example Patient Patient's information thatnever changes Information e.g. Gender, Birth Date Current medical Visitdate, female patient is pregnant or not at the information time ofvisit, patient is taking medication or not etc. Geography Patient'scountry of origin, region of origin Immune Status Optical density ofvarious diseases like HepA, Rubella etc and also the immuneinterpretation i.e. Positive, negative or susceptible for a disease.Environmental Patients education level, Type of toilet, water supply,conditions average people in house hold, number of rooms in house hold,type of water supply etc. Patients medical Has patient been hospitalizedbefore? If the patient history had diseases like measles, mumps etc andat what age, patient has vaccine record. Miscellaneous Information thatdoes not fall into any of the above

Current medical Visit date, female patient is pregnant or not at theinformation time of visit, patient is taking medication or not etc.

Geography Patient's country of origin, region of origin

Immune Status Optical density of various diseases like HepA, Rubella etcand also the immune interpretation i.e.

Positive, negative or susceptible for a disease.

Environmental Patients education level, Type of toilet, water supply,conditions average people in house hold, number of rooms in house hold,type of water supply etc. Patients medical history Has patient beenhospitalized before? If the patient had diseases like measles, mumps etcand at what age, patient has vaccine record.

Miscellaneous Information that does not fall into any of the above

Next, at 21A05, the logical groups can be prioritized in an order inwhich they are to be correlated. For example, one could choose thehighest priority logical groups that you want to find correlationsbetween (e.g. Immune Status v. Geography of the patient). This can bedone, for example, at 21A15, for all the logical groups. At 21A20,correlations can be sought. This can be done, for example, as follows:

-   1. obtain the percentage of people in the same geographical regions    that are immune, not immune or susceptible to diseases.-   2. try to find a region where the patient population has variation    in immunity status towards a disease. The reason for this is that if    89% of the people are Immune to Mumps in, say, N. America, this    means that there is not enough data for people who are not immune to    Mumps for evaluation. Whereas in South East Asia 67% are immune to    Rubella, therefore there is a large percentage of the population    (33%) that are either susceptible to Rubella or not immune. Thus    when there is difference in immune status in population in the same    region the remaining data can be explored to attempt to determine    the cause.-   3. Try to evaluate the above results by next logical group (i.e.    patient information—does the immunity status of a region differ by    gender?). Obtain the percentage of population by region and gender    that are immune, not immune or susceptible to the disease. If there    is a major variation in the percentage of male-female population for    same region that are immune or not immune, then there is a    discrepancy and the other data can then be explored to attempt to    determine a cause.-   4. Use a data mining tool to find the correlations of the next    logical group (i.e., for example environmental conditions on the    patients within the same region and gender and same immune status).-   5. Obtain the geometric means of the optical density of the various    diseases by geography and gender. This can determine if there is a    difference in the antibody level between genders living in the same    geographical regions. After seeking correlations at 21A20, if a    correlation is found at 21A25, it can be report at 21A27. The    process can continue until all groups have been searched, and    process flow ends, at 21A50.

In exemplary embodiments of the present invention, Oracle Data Miner canbe used, for example, as a tool for finding patterns in a database.

Using this tool, for example, there are different ways of findingcorrelations in the data.

Association Rules:

Oracle data miner uses Apriori Algorithm to find these associationrules.

Apriori Algorithm Details

-   -   Oracle Data Miner calculates the following two properties of        association rules:        -   Support: Support of an associating pattern is the percentage            of task-relevant data transactions for which the data is            true.    -   If A=>B    -   Support (A=>B)=Number of tuples containing both A and B    -   Total number of tuples        -   Confidence: Confidence is defined as the measure of            certainty or trustworthiness associated with each discovered            pattern.    -   If A=>B    -   Confidence (A=>B)=Number of tuples containing both A and B    -   Number of tuples containing A    -   Associations can be calculated in 3 steps:        -   1. Find all combinations of items, called frequent itemsets,            whose support is greater than minimum support.        -   2. Decide the minimum support and minimum confidence            required for choosing the rules. As the data set under            consideration was small we kept the minimum support=0.1 and            minimum confidence as 0.1 so that we do not miss any data            that might have any inverse co relation or strong co            relation.        -   3. Use the frequent itemsets to generate the desired rules.            Rules that satisfy both minimum support threshold and            minimum confidence threshold are called strong rules.            Reading the confidence and support get the rules that are            correlated.    -   For example, association rules generated for Chinese females who        are immune to rubella.    -   Some exemplary rules that can be generated can be, for example:

Rules Confidence Support If Hep A Non-Reactive then Rubella 1.000000.25532 Immune If Hep A Reactive then Rubella Immune 1.00000 0.74468 IfMeasles = Negative then Rubella Immune 1.00000 0.17021 If Measles =Positive then Rubella Immune 1.00000 0.80851 If Varicella = Positivethen Rubella Immune 1.00000 0.97872

-   -   Conclusions can be derived, for example, from the rules        generated by data miner.    -   Thus, Rule 1 means that 25% of the Chinese females who are        immune to Rubella are Hep A non reactive. The trustworthiness of        this statement is 100%.

Regression

-   -   Regression creates predictive models. The difference between        regression and classification is that regression deals with        numerical/continuous target attributes, whereas classification        deals with discrete/categorical target attributes. In other        words, if the target attribute contains continuous        (floating-point) values, a regression technique is required. If        the target attribute contains categorical (string or discrete        integer) values, a classification technique is called for.    -   The most common form of regression is linear regression, in        which a line that best fits the data is calculated, that is, the        line that minimizes the average distance of all the points from        the line.    -   This line becomes a predictive model when the value of the        dependent variable is not known; its value is predicted by the        point on the line that corresponds to the values of the        independent variables for that record. Oracle Data Mining        provides both linear and non-linear regression models.    -   Algorithm options: Support Vector Machines (SVM)    -   Support Vector Machine (SVM) is a classification and regression        prediction tool that uses machine learning theory to maximize        predictive accuracy while automatically avoiding over fit of the        data.

Geometric Mean: GM_(−y)=n√{square root over (Y₁Y₂Y₃. . . -Y_(m))}

-   -   The geometric mean of a set of positive data is defined as the        n^(th) root of the product of all the members of the set, where        n is the number of members.    -   Another way to calculate the geometric mean, which may aid in        statistical analyses, is to define it as the antilog of the mean        of the log values for a set of numbers.

Exemplary Data Mining Algorithm

Using the CIP database, the following exemplary algorithm was performed:

-   -   1. The logical groups were prioritized so that the immune status        (immune assay results) could be shown according to geography,        followed by Gender. This is shown in all examples of the data        mining (regression analysis, geometric means, and percent        support). All other logical groups could be examined later for        possible relationships that might help explain the observed        correlations.    -   2. Regression analyses were performed between all immune        variables at each geographic location, and by gender. Varying        cut-offs could be set to detect patterns of correlations from        tabulated correlation coefficients. For example, r values were        highlighted in the table where they were >0.05 or <−0.05, is        described above in connection with FIG. 20. This resulted a        possible association of Varicella with Measles and Mumps. These        r values were also graphed to facilitate any visualization, as        demonstrated.    -   3. Geometric means of immune assay results were calculated for        all geographic regions, and by gender. Graphic analyses were        performed, as demonstrated, to detect differences or        similarities between regions, as well as gender. For example,        there appeared to be a gender difference globally for Rubella        immunity in favor of males, and a lower immunity overall in        Southeast Asia. Hep A showed this gender difference only for        East Europe, with lower overall reactivity in both Southeast        Asia and East Europe.    -   4. Setting the confidence at 100% for different immune subsets        of a disease, different geographical regions were examined for        the percent support of the association with other variables. For        example, in each immune subset of Rubella (immune, low level,        susceptible) for Chinese females, the percent support was        determined for each of the other disease immune variables. The        graph (FIG. 20D) shows that there is a greater association of        Rubella immune support for positive Mumps, when compared with        Rubella low level or susceptible support. In another graph (FIG.        20E), positive and negative Mumps support was associated with        other diseases in different geographical locations. In this        case, there was a greater percent positive Mumps support for        Measles and Rubella in both East Europe and Sub Saharan Africa.    -   5. To enhance the chances of seeing meaningful associations,        regions where there was a lower incidence of immune status        result (e.g., <80%) were looked at, so that associations were        not just based on the fact that everyone has a particular        status. For example, if 95% of a population has a particular        status, then that status could likely be associated with        anything; however, as noted above, since there is 67% immunity        for Rubella in Southeast Asia, then there was enough        non-immunity to allow some possibility of detecting meaningful        associations.    -   6. Other logical groups were then now be examined for other        possible associations and explanations of previous associations.        For example, an association was graphically demonstrated between        positive Mumps support and university attendance in Southeast        Asia (FIG. 20F).

FIG. 21B depicts the exemplary pattern detection process flow of FIG.21A with additional expert system functionalities. Thus, at 21B60, foreach correlation, the hypothesis database can be searched for possibleexplanations of the given correlation. In general, this can be done, forexample, by using a Rules Database and Hypothesis as shown in FIG. 5A(and FIG. 2B) to map correlations to hypotheses according to definedrules. Once a set of hypotheses is generated, for example, at 21B65 eachhypothesis can be tested, to the extent possible, automatically, usingdata in the system database. Finally, at 21B67, a report can begenerated which lists the generated hypothesis and states, based onsystem data, if that hypothesis is corroborated, ruled out, orinconclusive, as next described.

Thus, in exemplary embodiments of the present invention, a HypothesisDatabase (and associated Rules Database) can function as a repositoryfor expert knowledge. When correlations are discovered by the system,these databases can be consulted to provide possible explanations as towhy certain correlations may exist. A Rules Database can, for example,map—as a function of its conditions on attributes, such as, for example,the database variables involved in the correlation—correlations tohypothesis already stored in a Hypothesis Database. For example, apossible sequence may occur as follows:

-   -   1. Database Searched.    -   2. Correlation found between Rubella and Varicella where        Antibody levels are directly proportional.    -   3. Consult Hypotheses Database for possible explanations.    -   4. Possible explanations:        -   a. Cross Reactivity (when exposed to one disease, build up            resistance to the other)        -   b. Multiple disease vaccinations; and        -   c. Patient living in an area where risk of exposure is            great.    -   5. System can automatically seek to verify whether each        hypothesis generated by the system, using the Rules Database and        Hypothesis Database, as above, is valid.        -   For example, the database records for the individuals            involved in the correlation can be checked for (i)            vaccinations for either or both of Rubella and Varicella,            and for (ii) living and/or socioeconomic conditions            conducive to exposure.        -   Next, the hypothesis and the            support/nonsupport/non-conclusiveness of each hypothesis can            be reported to humans, as shown in 21B67 of FIG. 21B.    -   6. After receiving a report, each correlation can be analyzed by        a human to determine if a new hypothesis should be added and fed        back into the Hypotheses Database; or if an existing hypothesis        is operative in the given context.

An example of this process can be illustrated with reference to FIG.20B, which presents levels of Rubella antibody concentration acrossvarious regions using data form the CIP database. First, the data wasgrouped by gender and region to determine if any trends were discovered.

As noted above, it was discovered that the females in Southeast Asia hadespecially low levels of Rubella antibodies. Upon further, lower level,geographic analysis it was found that the individuals from China werethe ones with low levels.

Possible hypotheses for this occurrence are, for example:

1. The females tested were never vaccinated for Rubella.2. The females tested were not exposed to Rubella via the generalpopulace.

As above, data already in the system can be used to examine the validityof each of these hypotheses.

In this way future correlations can, for example, be analyzed by thesystem itself to suggest possible reasons as to why trends or patternshave emerged.

Exemplary Automatic Pattern Detection Module

FIG. 21C depicts exemplary process flow for an exemplary automatedpattern detection module according to an exemplary embodiment of thepresent invention. With general reference thereto, the followingexemplary process can be implemented in exemplary embodiments of thepresent invention for such a module:

-   -   1. Prepare data for data mining. Most data mining algorithms        require data to be suitably transformed in order to produce good        results. Some common data transformations are: binning,        normalization, missing value imputation, and outlier removal. In        exemplary embodiments of the present invention, techniques used        for transforming the data can be, for example, selected based on        attribute data type, attribute value range, attribute        cardinality, and percentage of missing values for an attribute        or a record. (21C01, 21C03, 21C05)    -   2. Group the attributes of the data into different logical        groups like patient current immune status, patient history,        environmental conditions they lived in, geography, etc. (21C07)    -   3. As this is a data centric data mining system for diseases, a        focal point is to get the disease immune status relativity. The        attribute importance of each attribute can be found to rank them        in an ascending order to determine which attributes effect        patient's immune status to a particular disease. Attribute        Importance ranks the predictive attributes by eliminating        redundant, irrelevant, or uninformative attributes and        identifying those predictor attributes that may have the most        influence in making predictions. (21C07)        -   Example For rubella interpretation attribute the following            was found:

Attribute Importance Order RUBELLA_ANTIBODY_LEVEL 1 MEASLES_OPD_ZEUS 2ELECTRICITY 3 PT_GENDER 4

-   -   4. For each disease immune status (21C15) find a correlation        with all possible combinations of identified set of attributes        found in Step 3. (21C1B), (21C20) For example, a correlation        between Rubella interpretation and Rubella antibody level and        gender was found.    -   5. For each correlation a threshold can be with the help of a        (human) domain expert (21C30)    -   6. Compare the correlation found by the data miner with the        thresholds set. Verify the combination of attributes resulting        correlation with the disease immunity status with acceptable        threshold with the hypotheses database. If such relation already        exists remove this combination from further investigation.    -   7. If the correlation can not be explained by existing        hypotheses, analyze this attribute combination further for each        attribute's contribution to the correlation of the whole set of        attributes with disease immune status.    -   8. Derive association rules for the correlated attribute set        found from Step 7.    -   9. Check rules with the discovered set of rules for its        existence.    -   10. Analyze the Rules for determining the patterns in the data        set using line or curve fitting.    -   11. Report discovered pattern and verify with existing        hypotheses database.

For an initial exemplary analyses of the CIP database described above,where data was received in the form of an Excel spreadsheet, and datamining was accomplished using Oracle software, the following processeswere utilized. Similar processes can be implemented in exemplaryembodiments of the present invention. As described below in detail, in afollow-up series of analyses on the CIP database, fully automatedprotocols using the PipelinePilot™ software environment were created.

1. Initial Exemplary Analysis: Data Mining Steps 1.1. Data Preparation:

-   -   1. The data is received in .xls format.    -   2. The data then needs to be scrutinized for each column and        modified.    -    Example: some columns have data like “>250”. That needs to be        changed to some number greater than 250 (Scientist discretion)        since the data needs to be imported into the database as a        number and “>250” is not a number.    -   3. All the data is checked for valid values in the .xls sheet        before importing into the database.    -   4. Save the data.xls file as data.csv file (Comma separated        file).    -   5. Create the table using the data received using the script        createtable.sql.    -   6. Import the data from the .xls sheet into the new table using        the Immunoscore.ctl file.

1.2. Association Rules:

Association Rules provide the ability to show relationships that existin the data.

To find the association rules between the attributes of the data use theOracle Data Miner (ODM). For example, obtain association rules foreveryone that has a Measles Interpretation which is positive.

-   -   1. Create a view of records that have the attribute value        “measles interpretation” as “positive”.    -   2. Next go to Oracle Data Miner.    -   3. Click on Models>Association Rules>Build.    -   4. Name your Model and Click Next to continue.    -   5. Specify the location of the data used to build the model.        -   a. Schema: Select the schema containing the input table.        -   b. Input table: Select the table or view to use.        -   c. Records per Case: Select Single Record per case. (As each            patient record is 1 record in your view).    -    Click Next to continue.    -   6. ODM supports Apriori Algorithm to build Association Rules.        You can change the defaults.    -    Minimum Support: A real number between 0-1. Ask the scientist        for details.    -    Minimum Confidence: A real number between 0-1. Ask the        scientist for details.    -    Limit Number of Attributes in Each Rule: Number between 2-100.    -   After specifying the values Click Next to continue.    -   7. Select data preparation if any is required. Click Next to        continue.    -   8. Choose the attribute to include in your model. Click Next to        continue

9. Click Finish to queue your mining activity.

10. Once the mining activity is executed without error, get the rulesbased on Rule Length Ascending, Support Descending and ConfidenceDescending.

-   -   11. Export the rules to an .xls sheets.

1.3. Regression:

-   -   Regression Models provide the ability to predict numerical        attributes about data entities.    -   Steps:    -   1. Create an Oracle View from the main table with all the        numerical fields that you want in your regression model.    -   2. Click on ODM.    -   3. Click Model>Regression>Build    -   4. Name your Model. Click Next to continue.    -   5. Specify the location of the data used to build the model.    -    Schema: Select the schema containing the input table.    -    Input table: Select the table or view to use.    -    Records per Case: Select Single Record per case. (As each        patient record is 1 record in your view).    -    Click Next to continue.    -   6. ODM uses the Support Vector Machine algorithm for regression.        Change the values of the defaults by asking the scientists.        Currently defaults given by ODM are used.    -   7. Click Next to Continue.    -   8. Select Automatic Preparation option for your Model. Click        Next to continue.    -   9. Select the attribute you want to predict. Click Next to        continue.    -   10. Select all the attributes that must be in your model. Click        Next to continue.    -   11. Click Finish to queue the mining task on the server.    -   12. Once the task is done export the results to an .xls sheet.

F. Automated Data Mining

In exemplary embodiments of the present invention, partially or fullyautomated processing of the data in an exemplary database can beimplemented in various ways. The following describes five exemplarysoftware tools that can be used in exemplary embodiments of the presentinvention. These tools were created and then used to analyze an expandedversion of the CIP database. As noted above, approximately 20% of therecords of the CIP database (i.e., the first 330 records) are providedin Appendix A hereto for easy reference.

1. Exemplary Software Development Environment

The below described software tools have been implemented using ScitegicSoftware's Pipeline Pilot™ programming tool. The Scitegic approach,known as data pipelining, uses a data flow framework to describe theprocessing of data. FIGS. 21D-1 through 21D-37 illustrate the followingdiscussion.

Data pipelining is the rapid, independent processing of data recordsthrough a branching network of computational steps. It has severaladvantages over conventional technologies, including:

-   -   Flexibility: Each data record is processed independently,        allowing processing to be tailored to each record.    -   Speed: Highly optimized methods allow rapid analysis of        thousands (or millions) of data records.    -   Efficiency: Individual processing of data records limits memory        use so that many protocols can be executed simultaneously.    -   Ease of use: Protocols are easy to construct, with visualization        that exposes key data processing steps.    -   Integration: Data pipelining is a powerful tool for connecting        the different data sources, databases, and applications required        in the drug-discovery enterprise.

Thus, Pipeline Pilot provides environments to design, test, and deploydata processing procedures called protocols. A protocol is made up of aset of components that perform operations such as data reading,calculation, merging, filtering, and viewing. The connections betweencomponents define the sequence in which data is processed. Data fromfiles, databases, user input, and the Internet is merged, compared, andprocessed, according to the logic of the protocol.

Protocols are constructed with a graphical drag-and-drop interface. Thework environment is divided into windows. On the left, the Explorershows the contents of the database of available components and prebuiltprotocols. Additionally, a user can save new protocols in the databaseof components and publish them for enterprise-wide sharing and reuse. Onthe right, the workspace provides a way to create new protocols bydropping and connecting components.

The visual representation makes it simple to understand critical dataprocessing steps in a potentially complicated procedure. Components aredisplayed as function-specific icons clearly identified with descriptivelabels. Data records are passed between components through pipesrepresented by gray lines.

2. Client-Server Computing

Pipeline Pilot employs the client-server model of computing FIG. 21D-1.The professional client provides a way to create and edit components andprotocols, which are stored on the server. All protocols are executed onthe server. The server can also connect to resources on other machines,including files, databases and third party applications. Thisarchitecture provides a convenient way to integrate Pipeline Pilot anddistribute resources efficiently across different locations.

3. Third-Party Applications

Many Pipeline Pilot components integrate with third-party applicationssuch as, for example, Microsoft Word and Excel, Spotfire Decision Site,and Accelrys DS ViewerPro. A user can use these applications to readfrom, write to, and view data.

4. Extending Pipeline Pilot

Pipeline Pilot includes the following types of components that extendthe program's functionality:

-   -   Open Database Connectivity (ODBC): A user can access databases        such as Oracle, SQL Server, and MS Access that reside anywhere        on the user's network. ODBC components allow a user to select,        insert, delete, and update data.    -   Run Program Components: A user can execute an operating system        command on the server or on a user's client and extend the        functionality of a protocol to include any operation that a user        can invoke from a command line. For example, a user can write        out one or more data files, invoke the command line program to        work on these files, and then read the results back into the        protocol when the command execution is completed.    -   Simple Object Access Protocol (SOAP) services: A user can make        requests to a calculator or service that resides on Unix, Linux,        and other remote machines. The SOAP component sends the        necessary data to the SOAP server, collects the results, and        adds the data to the current record.    -   Scripting: A user can write scripts in one of the supported        syntaxes (VBScript, Perl, Java and Python) and use them in        protocols. The script has access to the contents of each data        record passed to the script component and to the relevant        protocol properties. This allows a user to write components such        as data readers, data writers, data filters, and calculators.    -   Visualization tools: Standard visualization tools such as        Internet Explorer, Excel, Spotfire, and Accelrys' viewer        software are integrated with Pipeline Pilot. Additional third        party viewers can be created by exporting the appropriate data        file and executing an operating system command to start up the        visualization software and read in the data file.

5. Integrating Protocols

Pipeline Pilot works with data and computer services that commonly existon user networks. After writing and publishing a protocol, a user canaccess the functionality of the protocol from environments other thanPipeline Pilot. For example, a user can run protocols on the PipelinePilot server from Internet Explorer, integrate a protocol into athird-party application such as Excel or Spotfire Decision Site, runprotocols from SOAP clients or from the command line on any computerwhere a Pipeline Pilot client or server is installed.

Next described are five exemplary automated data mining tools that wereimplemented in Pipeline Pilot, and that were used to analyze the CIPdatabase described above.

6. Data Mining Tool

This tool is designed to find correlations between variables, or fieldsin a database, across populations.

Run Application From:

Webport is the out of the box web-based client for Pipeline Pilot.Protocols can be deployed to Webport by saving them in the ProtocolsWebServices folder within Pipeline Pilot. Webport users simply go to a webpage and from this location protocols can be run. Users do not even needto know that Pipeline Pilot is being used; to the user it is simply aweb page where information can be obtained.

This paradigm is used for all protocols deployed via Webport.

Protocols

-   -   Protocols|Web Services\Wellstat_v3\Data Mining Tool        -   Protocols|Web Services\Wellstat_v3\Utilites\Filter Form with            Binning        -   Protocols|Web Services\Wellstat_v3\Utilites\Filtered Graph            with Binning

From Webport, all protocols are visible, except those protocols storedin a folder called Utilities. Because of this, all of the protocols thatare performing queries and calculations are “hidden” in the Utilitiesfolder. The Data Mining Tool protocol is outside of the Utilities folderand thus it is visible from Webport. This protocol is merely a shellthat points to the first protocol that needs to be run. On theprotocol's Implementation tab, the Protocol Form parameter points to theprotocol which will actually be run when the user clicks on the DataMining Tool in Webport. For the Data Mining Tool, this protocol is theFilter Form with Binning protocol. The data entered into the formcreated by Filter Form with Binning is passed to Filtered Graph withBinning.

A protocol is composed of components, which are the building blockswithin Pipeline Pilot. In general a component performs a particulartask. Multiple components can be collapsed into a Subprotocol. In thiscase, multiple components appear to be a single component as one looksat the protocol. When a user double-clicks on a subprotocol, it opens toshow the components that make up the subprotocol. In this way, asubprotocol can hide more complex logic.

Input:

Below is a screen shot of an exemplary embodiment of a data mining tooland its user interface. It is via this interface that a user can inputinformation that will be passed to the data mining tool. As can be seenfrom the figure, this particular version of the data mining tool iscalled “Data Munger With Binning.”

As can be seen from the exemplary screenshot in FIG. 21D-2, a user canselect which properties to group by, including the bin size for anygroups for which binning is desired. It is often convenient to bin byage in 5 or 10 year increments. An upper and lower correlation thresholdcan also be set. Options regarding output format can include, forexample, selecting to show the Value Table for each heat map and/or tocreate a PDF version of the output.

Lower and upper values for the standard deviations across groups canalso be specified. This is used to determine which attribute pairsappear in the Difference Between Groups output tab. This allows the userto set the cutoff values for which attribute pairs are “interesting”based on how much variation there is between the groups.

The protocol is set up to point to the necessary data files. Theparameter that points to the full data file is at the top level of theprotocol, so that the administrator of this application can easily setthe path to the full data source. The Full Data Source parameter isaccessed by clicking on the white space of the protocol, as illustratedin FIG. 21D-3.

Output:

There are up to 4 output tabs for this application:

-   -   Filters    -   PDF of Filters (optional)    -   Differences Between Groups    -   Whole Data Set Pie Charts for Each Group

The first output tab includes a Pie Chart showing the percentage of thepopulation that makes up each group for which a Heat Map was created.One correlation matrix and heat map are created for each data group,including only the selected attributes (such selected attributes areselected from the interface at the “Attributes to Include in Heat Map”field). Only correlation values within the specified range will beshown. If the Show Value Table option is selected, the values for theheat map are also displayed in a table. If the Create PDF option isselected, a second tab is created for a PDF version of the report.

FIG. 21D-4 depicts an exemplary data set distribution pie chart, anexemplary correlation matrix visualized as a heat map corresponding tothe segment of the database represented by the pink upper left piece ofthe pie, and a list of all the cells in the heat map and their actualcorrelation values.

In addition, a tab displaying the differences between groups for eachpair of attributes can be created as illustrated in FIG. 21D-5. Onlyattribute pairs that have a standard deviation within the upper andlower standard deviation thresholds will be displayed. The table can,for example, be sorted by decreasing standard deviation values, as shownbelow. This report allows the user to assess which attribute pairs maybe interesting based on the differences between the groups. For example,attribute pairs which have a larger standard deviation can be used tocreate different patient suggestion rules for different groups withinthe populations.

A final results tab can show the distribution of the data set for eachof the properties the data has been grouped by, as separate pie charts.Taking both properties together results in the two property pie chartshown in FIG. 21D-6.

Application Design:

As shown in FIG. 21D-7, there are three major steps involved in thisapplication:

-   -   Data preparation and grouping (red)    -   Creation of Correlation Matrices and Heat Maps (purple)    -   Creation of Reports (green)

FIG. 21D-8 illustrates the Data Mining Tool protocol, which includessubprotocols, illustrated in FIG. 21D-9, for Data Prep, Filtered HeatMaps and Difference Table creation and the creation of the DistributionPie Charts. The components shown are actually subprotocols, which arecomposed of multiple components to carry out the described tasks.

The data set is grouped so that each group (i.e., each segment of thedatabase that resulted from the “Group By” choices made via theinterface) of data can be acted on independently. For each group, acorrelation matrix is calculated and a Heat Map is used to display thisdata.

The correlation value for each attribute pair is then compared-acrossall of the groups and the standard deviation is calculated. This can beused to determine whether the correlation is “universal” across thedatabase, or only seen within certain defined segments.

The various pieces of information that are created are then placed intothe appropriate report outputs.

7. Single Patient Vaccine Recommendations

Run Application from:

Webport Protocols

-   -   Protocols|Web Services\Single Patient Vaccine Recommendations        -   Protocols|Web Services\Utilites\Single Patient Form        -   Protocols|Web Services\Utilites\Single Patient Treatment            Suggestions        -   Protocols|Web Services\Utilites\Create Learn Models (to be            run only once prior to these protocols)

The Single Patient Vaccine Recommendations protocol is outside of theUtilities folder and thus it is visible from Webport. This protocol ismerely a shell that points to the protocols that do the work. On theprotocol's Implementation tab, the Protocol Form parameter points to theprotocol which will actually be run when the user clicks on SinglePatient Vaccine Recommendations in Webport. For the Single PatientVaccine Recommendations, this protocol is the Single Patient Form. Thedata entered into the form created by Single Patient Form is passed toSingle Patient Treatment Suggestions. Create Learn Models must be runprior to using the Single Patient protocols.

Input:

A user browses to locate a file with a patient's data. An exemplaryWebport user interface is shown in FIG. 21D-10.

The protocol is set up to point to the necessary data files. Theseparameters are at the top level of the protocol, so that theadministrator of this application can easily set the paths to thespecified sources. These parameters are accessed by clicking on thewhite space of the protocol.

The Rules database is comprised of several files. Rules Source containsthe information about the Rule_ID and the Suggested Action. The RulesDefinition Source contains information about which conditions must bemet for each rule. And Conditions Source contains information aboutconditions that are used to describe the rules, for example,Pregnancy=1.

There are also files for internal and external references. The KeywordSource file documents the keywords for a each rule. The LiteratureSource file contains information about published documents, including afield containing Keywords that are used to link the document to rulesvia the Keyword Source file.

Output:

The result page has two sections. The Results table shows which rulesthe patient satisfies, including the Conditions, Suggested Action,Internal References and External References.

The Patient Data table shows the results for each assay, includingpredicted values. The percentiles for the entire data set, the patient'ssex, the patient's age group and the patient's region of origin are alsoincluded for all OD and TITRE properties.

Below is an image of an exemplary output for an exemplary individual inthe database, the first record of the CIP database provided in AppendixA below. In this exemplary embodiment, each rule in the Rule Database iscompared to the patient's test values. If the patient matches all of theconditions for the rule, the rule is considered “satisfied” and willappear in the Results Table, illustrated in FIG. 21D-12, including theconditions, suggested action, internal and external references for thatrule.

Application Design:

As can be seen in FIG. 21D-13, there are three major steps involved inthis application:

-   -   Data prep (red)    -   Determining Satisfied Rules (purple)    -   Create Tables for Report (green)

As part of the data preparation, the patient's data is read in and anyproperties that are missing can be predicted using learn models createdusing the Create Learn Models protocol (which must be run once prior torunning this protocol). This protocol, illustrated in FIG. 21D-14,creates a Learn Model for each property that is specified as a propertythat should be predicted. These Learn Models can be called from otherprotocols in order to calculate values for properties for patients thatare missing values.

The percentiles for the patient are calculated relative to the totalpopulation, his or her age bin, his or her sex and his or her region oforigin.

The patient's results are then used to determine which of the rules inthe Rules Database are satisfied, as illustrated in FIG. 21D-15. Thepatient data table includes all values in the original patient datafile, any predicted values, and the various percentile calculations. Forany rule that is satisfied, the information about the suggested action,internal and external references are joined in from the appropriatefiles. This information is displayed in the Results table.

8. Patient Population Rule Mining

Run Application from:

Webport Protocols

-   -   Protocols|Web Services\Wellstat_v3\Patient Population Rule        Mining        -   Protocols|Web Services Wellstat_v3\Utilites\Multiple Patient            Form        -   Protocols|Web Services Wellstat_v3\Utilites\Multiple Patient            Treatment        -   Protocols|Web Services Wellstat_v3\Utilites\Multiple Patient            Data Link Table

The Patient Population Rule Mining protocol is outside of the Utilitiesfolder and thus it is visible from Webport. This protocol is merely ashell that points to the protocols that do the work. On the protocol'sImplementation tab, the Protocol Form parameter points to the protocolwhich will actually be run when the user clicks on Patient PopulationRule Mining in Webport. For the Patient Population Rule Mining, thisprotocol is the Multiple Patient Form. The data entered into the formcreated by Multiple Patient Form is passed to Multiple PatientTreatment. The Multiple Patient Data Link protocol is run when the userclicks on a bar in any of the output graphs.

Input:

A user browses to locate file with patient's data and selects whatproperty to bin on. For properties that require binning, the bin size isalso entered. An exemplary interface appears below, which allows a userto select a file to read, and select the variable to graph by (with orwithout binning). In the example shown in FIG. 21D-16, the graphs aredrawn according to age, in bins of 20 years.

The protocol is set up to point to the necessary data files for theRules Database. parameters are at the top level of the protocol, so thatthe administrator of this application can easily set the paths to thespecified sources. These parameters are accessed by clicking on thewhite space of the protocol, as illustrated in FIG. 21D-17.

A description of the files that make up the Rules database can be foundin the section describing the Single Patient Vaccine Recommendations.

Output:

As shown in FIG. 21D-18, the output of the Patient Population RuleMining protocol consists of bar charts and a summary table withinformation about the standard deviation for each rule across thegroups.

The Bar Chart in FIG. 21D-18 shows the percent of the data set thatsatisfies each rule. There is also a table that shows the standarddeviation for each rule between the groups of data (for example thedifferent age bins). Rules that have a standard deviation greater than5% are highlighted. For each data group there is a Bar Chart showing thepercent of the data set that satisfies each rule. One patient can beincluded in multiple bars within each chart; the bars do not add up to100%. For example, if a patient satisfies both Rule 1 and Rule 2, thatpatient is included in both bars. There is also a bar displayingpatients that did not satisfy any of the rules. This is useful forunderstanding what percent of the patients have no suggested actions. Asillustrated in FIG. 19, clicking on a bar creates a new window thatshows the data records that make up that bar.

Application Design:

As can be seen in FIG. 21D-20, there are three major steps involved inthis application:

-   -   Get Rule Definitions and Calculate Rules for Each Patient (red)    -   Grouping of Patients and Calculation of Standard Deviation        (purple)    -   Creating tables and charts for reporting (green)

The determination of which rules are satisfied for each patient is donein the same way that the Single Patient rule determination is done. ABar Chart is created showing how many patients in the full data setsatisfy the criteria for each rule. There is also a table showing thestandard deviation for each rule across the patient groups.

Each patient is placed in the appropriate bin and the number of patientssatisfying each rule in each bin is calculated. Bar Charts are createdshowing the percent of patients within each group that satisfy eachrule. Clicking on a bar in any of the Bar Charts creates a new HTML pageshowing the data that makes up the bar.

9. Age Binned with DifferencesRun Application from:

Pipeline Pilot Client

This protocol can also be deployed from Webport if desired. A user canthen run the protocol without having to install the Pipeline Pilotclient.

Protocols

-   -   Protocols|Web Services Wellstat_v3\Utilites\Age Binned with        Differences

Input:

The protocol can be set up to point to the Full Data Source. Also theproperty to bin on, and the bin size can be entered via the protocol'stop level parameters, illustrated in FIG. 21D-22. These are accessed byclicking on the white space of the protocol. To have this report use adifferent property and bin size, the user can simply change theprop_list_bin parameter to the property of interest and the bin_sizeparameter to the desired bin size. Different bin sizes and/or propertiescan be selected and the protocol can be run multiple times, in order toilluminate how different groupings can affect the correlation values. Inexemplary embodiments of the present invention, a “brute force” binningby every binnable property of interest can be automatically launched viamultiple copies of this protocol operating in parallel and all thecorrelations thereby obtained out put to a cache or a list.Corresponding correlations can then be compared as to correlation valuesto isolate the best grouping relative to maximizing each suchcorrelation.

A user can also, set the upper and lower threshold for the standarddeviation to be used to filter the results. These parameters can bechanged to any desired values. In this example, the lower standarddeviation threshold is set to 0.50 which will limit the output to onlythose attribute pairs that have this level of deviation. The upperdeviation level is set at 2, which is the maximum deviation (−1 to 1).

Output:

As illustrated in FIG. 21D-23, each chart displays the correlationvalues for a pair of attributes across all of the bins, in this example,age with a bin size of 5 years. One XY Chart is created for each pair ofattributes. Each chart displays the correlation value for a differentattribute pair across the entire age spectrum (or whichever property theuser specifies). This output allows the user to quickly identify whichbins of the data have significantly lower or higher correlation values.Also, patterns of differences across groups can be identified by lookingat the output graphs as a whole.

Application Design:

As seen in FIG. 21D-24, there are three major steps involved in thisapplication:

-   -   Data prep and creation of correlation matrices    -   Calculating the Standard Deviation of the correlation values for        each Attribute Pairs    -   Creation of XY Charts

The entire data set is read in and the data binned on the specifiedproperty, in this example age binned with a bin size of 5, giving 12bins ranging from age 20-75. A correlation matrix is created for thedata for each age bin.

The correlation value for each pair of attributes can then be comparedover the entire age range and the standard deviation calculated. Also,the percent of the population contained within each bin is listed. Thisinformation can be used to identify bins with only a very small samplesize.

Attribute pairs can be, for example, filtered out if the standarddeviation of the correlation values across the age bins (value_StdDev)is not within the lower and upper stddev thresholds. Attribute pairsthat are not filtered out, are plotted in an XY Chart, as shown above.

10. Automated Data Mining

Run Application from:

Pipeline Pilot Client

This protocol could also be deployed from Webport if desired. This wouldallow users to be able to run the protocol without having to install thePipeline Pilot client.

Protocols

-   -   Protocols|Web Services Wellstat_v3\Utilites\Automated Data        Mining        -   Protocols|Web Services Wellstat_v3\Utilites\Automated            Run-Parallel

The Automated Run-Parallel protocol can be used to create a data cacheof the correlation value, attribute pair and group information for allpossible groups: The list of groups can be generated using a nested loopscript, as described below. Automated Data Mining can then use thiscache (described above) to create a report showing what grouping givesthe highest correlation value for a particular attribute pair.

In an exemplary implementation, the Automated Run-Parallel protocol wasrun to create all the data caches that are then used by Automated DataMining as inputs. This was done because running Automated Run-Parallelon a PC takes approximately three hours with the CIP data base as aninput. Thus Automated Run-Parallel is assumed to have been already beenpre-run as a data prep step by Automated Data Mining. Accordingly, preprocessing the database and creating the cache allows for runningAutomated Data Mining in real time. With more computational power,Automated Run-Parallel can simply be connected to Automated Data Miningand both protocols can run from start to finish in an automated fashion.

Input:

The protocol is set up to point to the Full Data Source. This parametercan be, for example, accessed by clicking on the white space of theprotocol, as illustrated in FIG. 21D-25.

Output:

FIG. 21D-26 illustrates the two outputs generated by this application. Atabular output shows all of the correlation values for a particularattribute pair across all groups, above the specified correlationthreshold and standard deviation threshold. An absolute correlationvalue threshold can be set by the user. This report allows users toquickly identify how best to maximize the correlation value for aparticular attribute pair.

The second output is created by starting with a particular group, forexample Measles_OD_DB_Int=0 and looking at what additional grouping canbe added to increase the correlation values for a particular attributepair. The graphs show the original correlation value as a green line andthe improved correlation values as red dots, one dot for each newgrouping. Each point on the graph has a mouse-over tooltip showing thegrouping and correlation value. A table is also created displaying thedata for each point on the graph.

Application Design:

As seen in FIG. 21D-27, there are four major steps involved in thisapplication:

-   -   Generation of all possible groups (blue)    -   Creation of one correlation matrix per group    -   Organization of the data by Attribute Pairs    -   Creation of tables and XY charts

Because generating the correlation matrix data is more time consumingthan the other data manipulation and generation of the reports, thisapplication has been broken into two protocols. However, this is not arequirement. The Automated Run-Parallel protocol performs the first twosteps. Automated data Mining performs the last two steps. AutomatedRun-Parallel generates a correlation matrix for each possible grouping,from single property groups (such as, for example, sex or age) to asmany groups as desired.

After the Automated Run-Parallel protocol is used to create correlationmatrices, the Automated Data Mining protocols can perform an analysisand generation of the reports. The Automated Run-Parallel protocol isdesigned to create one correlation matrix for each possible group. Thisdata can be used in a variety of ways.

A simple nested loop, illustrated in FIG. 21D-28, can be used, forexample, to create all of the possible data groups, including anybinning necessary. This nested looping script adds all unique datagroups to the prop_list property and the associated bin size informationto the bin_size property.

A file can be used to store the information about bin size for thesegroups. The file is composed of two fields, one for the name of theproperty and one for the bin size of that property. The grouping andcreation of the correlation matrices can be done in the same way as forthe other protocols listed above. Once the correlation matrix iscreated, the data contained in the table can be manipulated anddisplayed in a variety of ways. In the example illustrated in FIG.21D-29, this was implemented for up to a grouping of two properties, butany additional number of properties can be added as desired. For eachpossible group, the data can be used to create a correlation matrix andthe data is added to a data cache which is used in the report creation.Since there are many possible ways to work with the correlation matrixdata once it is created, the cache makes the correlation data accessiblewithout having to rerun the part of the application that creates thisinformation.

As can be seen, the Automated Run-Parallel protocol has a subprotocol,namely Subprotocol 1, at the end of pipe 2 (pipe 2 snakes around forease of viewing). This subprotocol is where a correlation matrix iscreated for a group. Subprotocol 1 detail is illustrated in FIG. 21D-30.

Detail of Filtered Heat Maps, a subprotocol in the third pipe ofSubprotocol 1, is illustrated in FIG. 21D-31.

Finally, subprotocol “for HTML”, illustrated in FIG. 32, (in the fourthpipe of the “Filtered Heat Maps” subprotocol of Subprotocol 1 of pipe 2of Automated Run-Parallel), is where the correlation matrix for eachgroup is created. The data is stored in a data cache so it can beaccessed by the Automated Data Mining protocol.

As noted, after Automated Run-Parallel has preprocessed the data, thenAutomated Data Mining begins processing. FIG. 21D-33 illustrates itshigh level processes. The Data Prep subprotocol takes the data from thecache created by Automated Run-Parallel and groups and cleans up thedata in preparation for creation of the report. This step could be movedto Automated Run-Parallel in order for all data preparation to be donein that protocol. Detail of the Data Prep subprotocol of Automated DataMining is illustrated in FIG. 21D-34.

The two subprotocols belonging to the Data Prep subprotocol of AutomatedData Mining are illustrated in FIG. 21D-35.

In the exemplary implementation the data is displayed in two ways, inHTML output and in a PDF file. The protocols to do this, Create Graphsand Create Tables, are illustrated in FIG. 21D-36.

7. Data Files Used in Pipeline Protocols Described Above

As noted above, to run the abovedescribed protocols a number of datafiles are drawn from. The exemplary files used were a set of Excelspreadsheets placed in a data folder, as illustrated in FIG. 21D-37.

As can be seen from the screen shot of the Data\Wellstat folder, thefiles used were CIP_data.xls, Conditions.xls, Literature.xls,Rule_Definition.xls, Rules.xls, and Rules_Keywords.xls. These files,their contents, and their functions are next described.

CIP_data.xls

This is the complete CIP database described above, the first 330 recordsof which are provided below in Appendix A.

Literature.xls

This is an internal database created to capture known information fromliterature and other sources regarding any conditions, diseases orsymptoms that a particular bioassay result, or a combination of suchresults, may be a marker for. An exemplary literature database wasconstructed to catalog the indications of bioassay results provided inthe CIP database

Copies of the Conditions.xls, Rules.xls, Rules_keywords.xls, andRule_Defininition.xls spreadsheets are provided in Exhibit A as well.

Conditions.xls The Conditions.xls spreadsheet was used in applyingdefined rules to the Single Patient Vaccine Recommendations protocol. Itcan also be used, for example, in Patient Population Rule Miningembodiments.

Rules.xls

The Rules.xls spreadsheet was used in applying defined rules to theSingle Patient Vaccine Recommendations protocol. It can also be used,for example, in Patient Population Rule Mining embodiments.

Rule_Definition.xls

The Rule_Definition.xls spreadsheet was used in applying defined rulesto the Single Patient Vaccine Recommendations protocol. It can also beused, for example, in Patient Population Rule Mining embodiments.

Rules_keywords.xls

The Rules_keywords.xls spreadsheet was used in applying defined rules tothe Single Patient Vaccine Recommendations protocol. It can also beused, for example, in Patient Population Rule Mining embodiments.

8. Complete Copy of Exemplary Pipeline Pilot Code (Provided on CD)

A complete copy of the Pipeline Pilot code is provided in Appendix Dhereto, which is submitted on a CD for ease of viewing. Furtherinformation regarding the contents of the CD is provided in Appendix Dbelow.

G. Exemplary Internal Hypothesis Database

Appendix B exemplifies the type of hypothesis database that can beconstructed using scientific articles and other literature regardingantibody markers to assist an ImmunoScore database user in buildingserological correlates for immunologic and other information stored inan exemplary database (such as, for example, the CIP database). So namedas a “hypothesis database,” the database can provide a user with allavailable information regarding two variables found to be correlated.The known information may, but often may not, explain the observedcorrelation. If it cannot, it can at least marshall whatever is knownregarding the variables implicated in the correlation.

In exemplary embodiments of the present invention, a hypothesis databasecan be constructed using a software spreadsheet application, such as,for example, Microsoft Excel, Word Perfect Quattro Pro® or Lotus 1-2-3®.Spreadsheet columns can be set up to record data obtained from thescientific articles as follows, for example:

Spreadsheet Column Information Recorded A Assay (or Marker) B AssayFamily C Assay Code D Assay Test Type E Sample Size F Percent (%)Support G P Value/Confidence Interval (CI) H Interpretation IDisease/Condition J Source K Notes

Upon reviewing a scientific article about an assay or marker, and makinga determination that it contains data suitable for entry into thedatabase (i.e., for example, that the study is not based on just one oronly a few study subjects, that the study reports P values and/orconfidence intervals, that the study provides numerical data supportingthe results reported), each specific assay or marker discussed by thearticle can be entered in a separate row of Column A of the spreadsheet.The assay family to which the assay or marker belongs can be entered inColumn B of the spreadsheet. Each specific assay (or marker) can beassigned an assay code, which can be entered in Column C of thespreadsheet. The specific kind of test performed to identify the assay(or marker), such as, for example, enzyme-linked immunoassay (ELISA),particle agglutination, sandwich enzyme immunoassay, neutralizationassay, solid phase enzyme immunoassay, molecular enzyme immunoassay, canbe entered in Column D. The total study sample size (including controls)can be entered in Column E of the spreadsheet. Numerical data supportingthe study results reported, such as, for example, the number of studysubjects out of the total subjects exhibiting the specific assay (ormarker), the percentage of study subjects exhibiting the specific assay(or marker), the number of study subjects having serum concentrationlevels less than, equal to, or greater than a certain amount, can beentered in Column F of the spreadsheet. P values and/or 95% ConfidenceInterval values correlating with the numerical data supporting the studyresults can be entered in Column G, such as where only one study resultis entered in Column F. Where more than one study result is entered inColumn F, p values and/or 95% Confidence Interval values, can beincluded in Column F together with the study results to which each suchvalue pertains. The interpretation of the study results by thescientific article's author(s), such as, for example, a particularantibody level may indicate an active infection with a particulardisease or acquired resistance to reinfection with a particular diseaseor may be associated with the presence of another disease, can beentered in Column H. The particular disease or condition in connectionwith which the assay (or marker) has been detected, such as, for examplethe detection of anti-diphtheria antibodies in HIV-1 infected subjects,can be entered in Column I. The source of the data entered in Columns Athrough I and in Column K, such as, for example, the author(s) and titleof the scientific article from which the data was obtained, as well asthe name of the journal, volume number, issue number (if available),page numbers and year in which the article was published, can be enteredin Column J. Other data reported in the specific scientific studyidentified in Column J, such as, for example, the geographical locationwhere the study was conducted, the nationality of the study subjects,the gender breakdown of the study subjects, whether the study examinedmore than one assay (or marker), and the age of the study subjects, maybe entered in Column K.

Appendix B, attached hereto, is a printout of the exemplary CIPhypothesis database created using Microsoft Excel. Because of printingconstraints, Columns A-H of each row of the database can be found onodd-numbered pages, while Columns I-K of each row of the database can befound on even-numbered pages.

The various automated data mining protocols described above (and whosecomplete code is provided in Appendix D), can, for example, draw uponthe information captured in the exemplary CIP hypothesis database, asnext described.

In one embodiment, an exemplary system can retrieve data entered in theexemplary CIP hypothesis database to perform a single patient analysis.For example, if serological assays indicate that an individual patienthas both filarial antibodies and strongyloides antibodies, the exemplarysystem can retrieve the data from the exemplary CIP hypothesis databaserows 2, 3, and 5. Row 2, for example, contains data captured from anarticle entitled “Predictive markers for development of strongyloidiasisin patient infect with both Strongyloidiasis stercoralis and HTLV-1,” byM. Satoh et al., and published in Clinical Experimental Immunology Vol.133: 291096 (2003) (Column J). The captured data includes, for example,that particle agglutination (Column D) was used to test HTLV-1 (aretrovirus, in the same class of virus as the AIDS virus, and isassociated with a rare form of blood dsycrasia known as Adult T-cellLeukemia/lymphoma (ATLL) and a myelopathy, tropical spastic paresis)antibody titer (Column A) in 44 (Column E) patients infected withStrongyloides stercoralis (a nematode) in Okinawa, Japan, and that 31patients (18 males, 13 females) were HTLV-1-positive and 11 patients (7males, 4 females) were HTLV-1-negative (Column K). Antibody titer in thedirect fecal smear-positive group (8,192 median ranging up to 28,672)was higher than in the direct fecal smear-negative group (4,096 medianranging up to 15,360) (P<0.05) (Column F). There was a significantcorrelation (p=+0.566, P<0.01) between the HTLV-1 proviral load and theantibody titer, and an inverse correlation between HTLV-1 proviral loadand EBNA (Epstein-Barr Virus) antibody titer (detected by anticomplementimmunofluorescence) (p=−0.387, P<0.05) indicating that increased HTLV-1proviral load was especially related to lowering of immune statusperhaps resulting in an increase of S. stercoralis load via immunityimpairment (Column K). The authors concluded, inter alia, that theactivity of HTLV-1 infection influences the results of direct fecalmethod of measuring HTLV-1 antibody titer in patients infected with bothS. stercoralis and HTLV-1 (Column H). Row 3, for example, contains datacaptured from an article entitled “L3 antigen-specific antibody isotyperesponses in human strongyloidiasis: correlations with larval output,”by N. S. Atkins, et al., and published in Parasite Immunology, Vol. 21:517-26 (1999) (Column J). The captured data includes, for example, thatimmunoblotting (Column D) was used to test for IgA antibody (Column A)in 34 patients (Column E) consisting of two groups of chronicallyinfected (for more than 30 years) ex-Far East Prisoners of War with andwithout detectable Strongyloides stercoralis larvae (Column K). IgAreactivity with six immunodominant S. stercoralis antigens wassignificantly elevated in individuals with undetectable larval output(Column F) (P<0.05 for three antigens and P<0.01 for one antigen)(Column G), and IgE recognition of four S. stercoralis antigens wassignificantly higher among individuals with larval output (Column F).The authors concluded, inter alia, that the results were consistent withan IgA-mediated immune effector mechanism in modulating larval output(i.e., inhibiting worm fecundity and egg viability) and IgE playing aprominent role in acquired resistance to reinfection (Columns H and K).The authors postulated that parallel upregulation of IgE and IgG4responses to certain antigenic components suggests IgG4 blockage ofIgE-mediated allergic responses and may be central to establishment andpersistence of asymptomatic chronic strongyloidiasis (Column K). Row 5,for example, contains data captured from an article entitled “Detectionof filaria-specific IgG4 antibodies and filarial DNA, for the screeningof blood spots for Brugia timori,” by P. Fischer, et al., and publishedin Annals of Tropical Medicine & Parasitology 99(1): 53-60 (2005)(Column J). The captured data includes, for example, that Brugia rapid(BR) (an antibody-detection dipstick test) and PCR (polymerase chainreaction)-based assays (Column D) were used to test for IgG4 antibody(Column A) in 66 individuals (Column E) from Alor island, East NusaTenggara, Indonesia (which is an area highly endemic for Brugia timori)(Column K). Thirty-seven (37) of the 66 individuals (56.1%) offilter-paper blood spot eluates were positive using the BR test (32strongly and 5 weakly), while the plasma samples of 47 of the 66individuals (71.2%) were positive; 9 (23.4%) of the BR filter-paperblood spot eluates positives were PCR-positive (Column F). The authorsconcluded, inter alia, that, in general, the presence of microfilaremiais associated with relatively high titers of anti-filarial IgG4 (ColumnH).

In another embodiment, the analysis of population heat maps can besupplemented by drawing upon the information captured regarding theassays (or markers) implicated in a given correlation from the exemplaryCIP hypothesis database. For example, if analysis of population heatmaps generated by the Pipeline Pilot software reveals a correlationbetween hepatitis C virus (HCV) and schistosomiasis, as is describedbelow, the exemplary system can retrieve the data from the exemplary CIPhypothesis database on schistosomiasis from row 4 and on hepatitis Cfrom rows 44, 47-48, 51, 61-124, 136, and 148-150. Row 4, for example,contains data captured from an article entitled “The antibody responsesto adult-worm antigens of Schistosomiasis haematobium, among infectedand resistant individuals from an endemic community in southern Ghana,”by Y. Osada, et al., and published in Annals of Tropical Medicine &Parasitology, Volume 97(8): 817-26 (2003) (Column J). The captured dataincludes, for example, that enzyme-linked immunoassay (ELISA) (Column D)was used to test for IgE, IgG, IgA and IgM antibodies (Column A) in 27individuals (Column E) infected with Schistosoma haematobium (11 endemicnormal subjects; 16 patently infected subjects) in Okyerko, the Gomoadistrict of Ghana, where S. haematobium is endemic (Column K). Endemicnormal subjects were generally older than patently infected individuals(P<0.001); the male-female ratio was higher in patently infectedindividuals than in endemic normal individuals, although the differencewas not statistically significant (P>0.05); and for patently infectedindividuals and for patently infected and endemic normal individualscombined, S. haematobium egg output was negatively correlated with thewater-contact index (Column K). Endemic normals have similar levels ofIgM antibody, higher levels of IgA (P<0.05) and lower levels of IgE(P<0.01) and IgG (P<0.05) than patently infected individuals; forcombined endemic normals and patently infected individuals, males hadlevels of IgM, IgA and IgE similar to that of females, but significantlyhigher levels of IgG (P<0.01); when the patently infected and endemicnormals were considered as a single group, S. haematobium egg outputspositively correlated with levels IgE (P 0.01) and IgG (P<0.001); in thepatently infected group alone, only the correlation with IgG wasstatistically significant (P<0.01); the P values for the positivecorrelation of IgG and IgE in endemic normals only, patently infectedonly, and endemic normals and patently infected combined were 0.01, 0.05and 0.001, respectively; positive correlations between levels ofspecific IgG and IgE were statistically significant only when data forendemic normals and patently infected individuals was pooled (P<0.05);levels of specific IgA and IgE were positively correlated in thepatently infected group (P<0.01), but not in the combined group, andlevels of specific IgA and IgG were positively correlated in the endemicnormals (P<0.05), but not in the combined endemic normal and patentlyinfected group (Column F). The authors concluded, inter alia, that therelatively high levels of IgG and IgE may directly reflect “active”current infection or that the high level of specific IgG seen in thepatented infected group may reflect the presence of blocking antibodies;and that IgE and IgG antigens can be used as markers to reflect currentinfection intensity and that anti-worm antibodies do not act asprotective antibodies in the natural course of urinary schistosomiasis(Column H).

A sampling of the kind of data regarding hepatitis C captured from thearticles in rows 44, 47-48, 51, 61-124, 136, and 148-150, is exemplifiedby the data from, for example, rows 44 and 51. Row 44, for example,contains data captured from an article entitled “Viral markers and useof factor products among Finnish patients with bleeding disorders,” byF. Ebeling, et al., and published in Haemophilia 7: 42-46 (2001) (ColumnJ). The captured data includes, for example, that ELISA (Column D) wasused to test for hepatitis C antibody (Column A) in 193 patients (ColumnE) with bleeding disorders (hemophilia A or B, type 3 von Willebranddisease or factor XIII deficiency) in Finland, 179 (93%) of whom weremales (Column K). Fifty-one percent (51%) of the patients were anti-HCVpositive (Column F), and the authors interpreted this positivity asbeing associated with blood transfusions (Column H). Row 51, forexample, contains data captured from an article entitled “The clinicalepidemiology and course of the spectrum of renal diseases associatedwith HIV infection,” by Lynda A. Szczech, et al., and published inKidney International, Volume 66: 1145-52 (2004). The captured dataincludes, for example, that hepatitis C antibody (Column A) wasidentified in 89 HIV-infected patients who underwent renal biopsy duringthe course of clinical care at six major medical centers in the UnitedStates, 47 of whom had lesions other than HIV-associated nephropathy(HIVAN) and 42 of whom had HIVAN lesions (Column K). Patients withlesions other than HIVAN were less likely to be black ( 37/47 vs. 42/42,P=+0.02), less likely to have hypertension ( 24/26 vs. 31/24, P=0.03),more likely to have greater creatinine clearance at the time of biopsy(60.6 vs. 39.0 mL/min, P=0.008), and have greater CD4 lymphocyte countat time of biopsy (287 vs. 187 cells/mL, P=0.04); all patients withHIVAN were black (Column K). Unadjusted survival curves demonstratedbetter renal survival among patients with non-HIVAN lesions (P=0.002)(Column K). Patients with lesions other than HIVAN tended toward beingmore likely to be infected with hepatitis C ( 25/41 vs. 17/41, P=0.08);and the presence of hepatitis C antibody was associated with a fastertime to the institution of renal replacement therapy (HR 2.60, P=0.01)(Column F). The authors concluded, inter alia, that patients withnephropathy other than HIV-associated nephropathy were more likely tohave hepatitis C antibodies (Column H).

In another example, if analysis of population heat maps generated by thePipeline Pilot software reveals a correlation between measles andhepatitis, as is described below, the exemplary system can retrieve thedata from the exemplary CIP hypothesis database on measles from rows 9,12, 27-31, and 34 and on hepatitis from rows 44-53, and 55-157.

A sampling of the kind of data regarding measles captured from thearticles in rows 9, 12, 27-31, and 34, is exemplified by the data from,for example, rows 27 and 34. Row 27, for example, contains data capturedfrom an article entitled “Measles antibody in vaccinated humanimmunodeficiency virus type 1-infected children,” by Stephen M. Arpardi,et al., and published in Pediatrics, Volume 97(5): 653-57 (1996) (ColumnJ). The captured data includes, for example, that ELISA (Column D) wasused to test for measles antibody (Column A) in 81 perinatallyHIV-infected children with prior documented receipt of measles vaccine(i.e., monovalent measles or combination measles-mumps-rubella), with amedian age of 42 months at the time of study, and a median age of 14months at first vaccination (Column K). Overall, 58 subjects (72%) hadmeasles neutralization assay antibody titers >1:5 (Column K). Childrenstudied within 1 year of vaccination were significantly more likely tohave detectable measles antibodies than those studied more than 1 yearafter vaccination (83% vs. 52%, P<0.01), and the proportion of childrenwith detectable measles antibody was greatest for children with noevidence of immunosuppression and lowest for children with severeimmunosuppression (Column F). The proportion of children with detectablemeasles antibody was significantly lower for children with CD8 greaterthan the 95^(th) percentile for their age and for children with lowerCD4/CD8 ratios (Column F). The authors concluded, inter alia, that theprevalence of measles antibody in vaccinated HIV-infected children wasconsiderably lower than in healthy children (only 72% of previouslyvaccinated children had measles antibody detected by neutralizationassay, in contrast to 95% among healthy children), that the proportionof children with detectable measles antibody declined over time, andthat revaccination for seronegative HIV-infected children was not likelyto be effective once immunodeficiency was established (Column H). Row34, for example, contains data captured from an article entitled“Clinical presentation of subacute sclerosing panencephalitis in PapuaNew Guinea,” by Charles S. Mgone, et al., and published in TropicalMedicine & International Health, Volume 8(3): 219-27 (2003) (Column J).The captured data includes, for example, that enzyme immunoassay (EIA)(Column D) was used to test both serum and cerebrospinal fluid formeasles-specific IgG antibody (Column A) in 95 children with a clinicaldiagnosis of subacute sclerosing panencephalitis (SSPE) from withinEastern Highlands province of Papua, New Guinea (Column K). Twenty-eightchildren had had measles, 28 had not and 14 were uncertain; the verifiedmean age for contracting measles was 8.8±2.7 months, the majority ofchildren who had measles had contracted the infection in the first yearof life, the mean age at which SSPE was manifested was 7.9±2.6 years,and the time between the measles illness and the onset of SSPE was6.2±1.9 years (Column F). The authors concluded, inter alia, that hightiters of measles antibodies are found in the serum (>200,000) and/orcerebrospinal fluid (>2000) of SSPE children (Column H), and that,although the pathogenesis of SSPE is not fully understood, theaccumulated evidence suggests that it arises from persistence of alteredmeasles virus in the brain (Column K).

A sampling of the kind of data regarding hepatitis captured from thearticles in rows 44-53, and 55-157, is exemplified by the data from, forexample, row 133. Row 133, for example, contains data captured from anarticle entitled “A Seroprevalence Survey of Hepatitis B Markers amongHaitians in a Southwest Florida Farming Community,” by Michael D.Malsion, et al., and published in American Journal of Public Health,Volume 75(9): 1094-95 (1985). The captured data includes, for example,that radioimmunoassays (Column D) were used to test for HBsAg (hepatitisB surface antigen), anti-HBc (antibody to hepatitis B core antigen) andanti-HBs (antibody to hepatitis B surface antigen) (Column A) in 123Haitian women attending a prenatal clinic in Immokalee, Fla., a small,migrant farm-worker community during a 12-month period (Column K).Twenty-eight out of 51 (55%) Haitian mothers had one or more HBVmarkers; 2/51 (4%) asymptomatic mothers were HBsAg positive and childrenof these women (aged 1 to 3 years) were negative for all HBV markers;4/7 (57%) of the infants less than 6 months old and their mothers wereantibody positive, but none were HBsAg positive; 3/54 (6%) of thechildren 1-4 years old were antibody positive and none wereHBsAg-positive (Column F). The authors concluded, inter alia, that alarge proportion of the Haitian women in Immokalee have been previouslyinfected with HBV, and a small percentage are probably chronic HBsAgcarriers; of the 7 HBsAg-positive women identified, only 1 was HBeAgpositive; the infants of 2 HBsAg-positive women were negative for HBVmarkers, suggesting that the risk of perinatal transmission of hepatitisfor infants born to HBeAg-negative women is low; and the small portionof children 1-4 years old with HBV markers suggests the risk forsib-to-sib transmission in this age group is also low (Column H).

If a correlation is found that is hitherto unknown (and thusinteresting), a user will want to try to best explain the correlation aswell as why it is or is not uniform across various segments of a givenpopulation. Providing such a user with both information from internaldatabases, as well as information obtained from external databases, isthus very useful. Such internal database could include, for example, theexemplary CIP hypothesis database. External databases can include allknown sources of immunological, medical, epidemiological, and relatedinformation, such as NIH, Medline, PubMed, patent databases, etc. Anexternal database search can be launched, for example, using a real timeexternal text analytics tool, such as is provided as a protocol in thePipeline Pilot Software described above.

Thus, in exemplary embodiments of the present invention, after runningan automatic data mining program on a given database, the results in theform of a set of “interesting” correlations can, for example, begenerated. These correlations can then be further automaticallyprocessed, by running an internal as well as external text search onthem to associate with each variable in the correlation knowninformation that hypothesizes a basis for the correlation found. If nosuch hypothesis is available, which is generally the case for trulycounter-intuitive and novel correlations, such information as is thenknown regarding each of the variables in the correlation can bemarshalled via such searching, and can be output in a report of a user.This can assist such a user in formulating a hypothesis or in ruling oneout.

H. Explanation and Basis of Exemplary Rules Created for Processing CIPDatabase

As described in the Automated Data Mining section above, a rulesdatabase was created for processing individual records or populationsfrom the CIP database. This was called “Rules.xls” and is provided inAppendix A below. The following describes the rationale and basis forvarious ones of these rules.

For example, Table 1 displays an exemplary set of the exemplary CanadianImmigrant Population assays and their interpretations.

TABLE 1 CIP Assays and Possible Interpretations CIP Assay PossibleInterpretation Measles +/−/equivocal Mumps +/−/equivocal Rubella+/−/equivocal Varicella +/−/equivocal Tetanus OD → IU → nointerpretation Diphtheria OD → IU → no interpretation Cytomegalovirus(CMV) +/− Strongyloides +/−/equivocal Filaria +/−/equivocal Schistosoma+/− Hepatitis A +/− Hepatitis B HBc Ab +/− HBs Ab + (not apparent) HBsAg reactive/non-reactive HBe Ab reactive/non-reactive HBe Agreactive/non-reactive Hepatitis C HCV Ab +/−/“grayzone” = equivocal HCVPCR +/− HCV LIA +/− IL-1 alpha reactive/non-reactive IL-1 betareactive/non-reactive IL-2 reactive/non-reactive IL-4reactive/non-reactive IL-5 reactive/non-reactive IL-6reactive/non-reactive IL-8 reactive/non-reactive IL-10reactive/non-reactive IL-12p70 reactive/non-reactive IL-13reactive/non-reactive IL-15 reactive/non-reactive IL-17reactive/non-reactive IL-23 reactive/non-reactive IFN-gammareactive/non-reactive TNF-alpha reactive/non-reactive TNF-betareactive/non-reactive

Exemplary Rules for Measles-Mumps-Rubella (MMR) Testing

All patients are tested for measles-mumps-rubella antibodies. Positiveresults require no further action. Negative or equivocal results in anyone of these assays would indicate need for immunization with MMRvaccine. Equivocal results in any one assay should require a boosterimmunization, while negative results would indicate a series of twoimmunizations.

As with all live virus vaccines, women known to be pregnant should notreceive the MMR vaccine, and pregnancy should be avoided for four weeksfollowing vaccination with MMR. However, women who are breast-feedingcan be vaccinated. Children and other household contacts of pregnantwomen should be vaccinated according to the recommended schedule.

Severely immunocompromised persons should not be given MMR vaccine. Thisincludes persons with conditions such as congenital immunodeficiency,AIDS, leukemia, lymphoma, generalized malignancy, and those receivingtreatment for cancer with drugs, radiation, or large doses ofcorticosteroids. Household contacts of immunocompromised people shouldbe vaccinated according to the recommended schedule.

Although persons with AIDS or HIV infection with signs of seriousimmunosuppression should not be given MMR, persons with HIV infectionwithout symptoms can and should be vaccinated against measles.

Exemplary Rules for Varicella Testing

All patients are tested for varicella antibody. Positive results requireno further action. Children under the age of 13 with no history ofchicken pox and negative results should receive two immunizations fourto eight weeks apart. Children with equivocal results and no history ofdisease should receive one booster immunization and be retested afterone year.

Herpes zoster (shingles) is a currently a risk for patients over 60years of age. Patients in that age category, who do not have animmunodeficiency, and have a negative or equivocal result, shouldreceive one dose of zoster vaccine specifically formulated for adults.An immunodeficiency in these individuals would include a history ofprimary or acquired immunodeficiency states including leukemia;lymphomas of any type, or other malignant neoplasms affecting the bonemarrow or lymphatic system; or AIDS or other clinical manifestations ofinfection with human immunodeficiency viruses. This vaccine is notindicated for women of child-bearing age and should not be administeredto pregnant females.

Exemplary Rules for Tetanus Testing

All patients should be tested for tetanus antibody. Patients with lessthan the minimum protective level of 0.01 International Units (IU)/mLshould be given a booster dose of tetanus vaccine. Individuals overseven years of age receive the vaccine in combination with diphtheriavaccine (Td). Those children younger than seven years of age can beboosted with a vaccine containing a pertussis component (DTaP). Peoplewho had a serious allergic reaction to one dose of tetanus toxoid shouldnot receive another. Persons with a moderate or severe acute illnessshould postpone receiving the vaccine until they are improved.

Exemplary Rules for Diphtheria Testing

All patients should be tested for diphtheria antibody. Patients withless than the minimum protective level of 0.01 IU/mL should be given abooster dose of diphtheria vaccine. Individuals over seven years of agereceive the vaccine in combination with tetanus vaccine (Td). Thosechildren younger than seven can be boosted with a vaccine containing thepertussis component as with the tetanus vaccine described above. Peoplewho have had a serious allergic reaction to one dose of DTaP, DT, Td, orTdap vaccine should not receive another. Persons with a moderate orsevere illness should postpone receiving the vaccine until theircondition has improved.

Exemplary Rules for Cytomegalovirus (CMV) Testing

All patients should be tested for CMV antibody. A negative result wouldrequire no specific action.

As previously described, a positive result may be indicative of possibleimmunosupression in these individuals as they age. Periodic diagnosticmonitoring of these patients with positive antibody levels should betriggered and furthermore, increase in frequency as they age.

As annual flu immunizations and periodic booster immunizations againstpneumococcal infection have little effect in the elderly with high CMVtiters, patients with very high levels of CMV antibody potentially mayhave different vaccine recommendations than the general population:

-   -   Patients over 65 years of age with repeatedly high CMV levels        should have their younger contacts (children and grandchildren)        vaccinated annually against influenza. In addition, these same        contact individuals should have their pneumococcal vaccinations        up-to-date. Depending on recommendations by the ACIP and AAP,        these individuals may possibly be recommended to not receive the        annual influenza immunization.    -   Patients over 65 years of age with repeatedly high CMV antibody        levels should also be regularly screened for routine        Th1/Th2/Treg/Th17 cytokine levels to assess immune balance and        any autoimmune diseases should be closely monitored by regular        ImmunoScore diagnostic screening.    -   Patients 50-65 years of age with repeatedly high CMV levels        should have regular influenza vaccinations and be checked every        2-5 years for antibody levels to pneumococcal polysaccharides        used in vaccines currently marketed. Similar to the elderly        group, patient contacts (children and grandchildren) should also        have up-to-date influenza and pneumococcal immunizations.    -   Patients 50-65 years of age with repeatedly high CMV antibody        levels should also be regularly screened for cytokine levels as        described above. Onset of autoimmune disease and flares should        be monitored closely by the health care providers.    -   Patients younger than 50 years of age with repeatedly high CMV        antibody levels should be regularly immunized against influenza,        and should be examined every 2-5 years for antibody levels to        pneumococcal polysaccharides used in current vaccines.    -   Patients younger than 50 years of age with repeatedly high CMV        antibody levels should be regularly screened for cytokine        levels.    -   Patients younger than 50 years of age with repeatedly high CMV        antibody levels may be an ideal group to test with        immunotherapeutics under development.

Exemplary Rules for Strongyloides Testing

All patients should be tested for Strongyloides stercoralis antibody.Negative tests require no follow-up action. A positive or equivocalresult would indicate the further examination of stool samples.Microscopic examination of stool specimens is insensitive; estimates fora single positive stool examination in cases of uncomplicated infectionrange from 0 to 66%. To overcome this lack of sensitivity, investigatorshave recommended examination of up to seven stool specimens; use of moresensitive and labor-intensive methods of stool examination; use of agarplate cultures; and collection of alternate specimens, such as duodenalaspirates.

Due to these difficulties in diagnosing the progress of strongyloidesinfections, reported efficacies of drugs used to treat strongyloidesinfection vary widely. Chemotherapy is advocated and considered aneffective control measure for the reduction of morbidity resulting fromintestinal nematode infection. The current drug of choice forstrongyloides is the benzimidazole compound, thiabendazole. This drugrequires a three day regimen. Another drug being considered isivermectin, which may be preferable, because it requires only one dose.Post-treatment follow-up testing recommendations would require stoolsampling 30 days post-treatment.

Exemplary Rules for Filaria Testing

All patients should be tested for antibody to filarial worms, Wuchereriabancrofti and Brugia malayi. Negative tests require no follow-up action.

Filarial worms reside in the lymphatic system, and therefore likely havea great impact on the body's immune defense systems. Though infection isusually acquired early in childhood, filarial disease can take years tomanifest. Many infected individuals develop no clinical symptomsunderscoring the need for routine diagnostic testing in endemic areas.

Individuals that are positive for filaria antibody should be treatedwith a combination of albendazole with either diethylcarbamazine orivermectin. This treatment has been shown to be over 99% effective inremoving microfilariae from the blood for a full year after treatment.Seropositive individuals should be screened one year after treatment.

Patients that have equivocal antibody levels should be re-tested afterseveral weeks to determine if they are in the early stages of infection.If they then have a positive result, they should be treated as above. Ifnegative, no further action is required.

Exemplary Rules for Schistosoma Testing

All patients should be tested for worms that cause schistosomiasis.Patients that have negative tests require no follow-up action.

Patients are infected by contact with water used in normal dailyactivities such as personal or domestic hygiene and swimming, or byprofessional activities such as fishing, rice cultivation andirrigation. Schistosomiasis is endemic in 74 tropical developingcountries. Some 600 million people are at risk of becoming infected.Population movements and refugees in unstable regions contribute to thetransmission of schistosomiasis.

Patients who have a positive antibody result should be treated dependingon the manifestation of illness. Praziquantel is used to treat all formsof schistosomiasis. Oxamniquine is used exclusively to treat intestinalschistosomiasis, and metrifonate is effective for the treatment ofurinary schistosomiasis. No further intervention is typically neededfollowing treatment for 2-5 years. According to the World HealthOrganization (WHO), treatment of schistosomiasis must be accompanied byhealth education to preclude re-infection.

Exemplary Rules for Hepatitis A Virus (HAV) Testing

All patients should be tested for total antibody to HAV. HAV testingevaluates total antibody levels to HAV among patients. If a patienttests positive for anti-HAV, a further test for anti-HAV IgM isperformed to determine presence of acute infection. Currently approvedassays do not detect less than 100 mIU/mL of antibody, yet levels as lowas 10 to 20 mIU/mL are thought to confer protection. The CDC does notcurrently recommend revaccination of healthy individuals withundetectable antibody levels.

The most likely time for an HAV-infected person to spread HAV to othersis during the two weeks before the infected person develops symptoms.Clearly, if a person doesn't even know that he or she is infected, itmakes it difficult to protect others from getting the infection. Therisk of spreading HAV becomes smaller over time and can still be presentone week or longer after symptoms develop (e.g., yellowing of skin andeyes). Infants are more likely to be capable of spreading HAV for longerperiods of time.

If an unvaccinated person thinks that he or she might have been exposed,he or she should call their health professional immediately to schedulean appointment to determine whether a real exposure has occurred andwhether Ig should be administered. Ig is a concentrated dose of humanantibodies that includes anti-HAV. In most cases, this preparation canprotect an exposed person from developing HAV infection.

People at increased risk for exposure to HAV infection or those who aremore likely to get seriously ill if infected with HAV should bevaccinated. According to CDC recommendations, these individuals include

-   -   All children at age 1 year (12-23 months)    -   People aged 12 months or older who are traveling to or working        in any area of the world except the United States, Canada,        Western Europe, Japan, New Zealand, and Australia    -   Men who have sex with men    -   Illegal drug users, both oral and injecting    -   People who have blood clotting disorders    -   People who work with HAV-infected primates or with HAV in a        research laboratory setting. No other groups have been shown to        be at increased risk for HAV infection because of occupational        exposure.    -   People with chronic liver disease are not at increased risk of        getting infected, but are at risk for developing serious        complications if they get infected.    -   Any person who wishes to be immune to hepatitis A

Hepatitis A vaccine is NOT routinely recommended for healthcare workers,sewage workers, or daycare providers. Children who are not vaccinated byage two years should be vaccinated as soon as feasible.

Exemplary Rules for Hepatitis B Virus (HBV) Testing

All patients are tested for anti-HBs (antibody to hepatitis B surfaceantigen) and anti-HBc (antibody to hepatitis B core antigen). Dependingon the results of these two tests, additional testing will be performedas follows (FIG. 1):

POSITIVE/POSITIVE If the patient has positive results for both tests,the HbsAg (hepatitis B surface antigen) is measured. This test is doneto determine whether patients are chronic carriers of HBV infection. Incases in which the HBsAg test is negative, no further tests areperformed, and the results are interpreted as indicating a patientexposed to hepatitis B virus that has cleared the virus. If the HBsAgtest is positive, the person is identified as a chronic carrier ofhepatitis B. Patients positive for HBsAg are further tested for thepresence of antibody for hepatitis B core antigen IgM (IgM anti-HBc),hepatitis B e antigen (HBeAg), and antibody to hepatitis B e antigen(anti-HBe) to evaluate the level of viral replication.

POSITIVE/NEGATIVE Patients testing positive for anti-HBc only are thentested for HBsAg. Whether the HBsAg test is positive or negative, theplasma is further tested for IgM anti-HBc, HBeAg, and anti-HBe toevaluate the level of viral replication. Any positive result in thisseries of tests indicates that the patient is a chronic HBV carrier.

NEGATIVE/POSITIVE Patients testing negative for anti-HBc but positivefor anti-HBs may have been either vaccinated or exposed naturally toHBV, or this may represent a false-positive result (no exposure). Theseindividuals will be retested the following year.

NEGATIVE/NEGATIVE Patients testing negative for both anti-HBs andanti-HBc have not been exposed to HBV and will be retested the nextyear.

It is estimated that about 1 out of 3 of the nearly 1 million Americanswith chronic HBV infection acquired their infection as infants or youngchildren. Those with chronic HBV infection are most likely to spread theinfection to others. Infants and children who become chronicallyinfected have an increased risk of dying prematurely from liver canceror cirrhosis.

In contrast to other vaccine-preventable diseases of childhood, HBVinfection in infants and young children usually produces no symptoms.Thus, the small number of reported cases of hepatitis B among childrenrepresents the tip of the iceberg of all HBV infections in children. Forevery child with symptoms of hepatitis B, there are at least 100HBV-infected children with no symptoms—hence the increased risk tospread the infection to others without knowing it.

Second, early childhood infection occurs. About 16,000 children under 10years of age were infected with HBV every year in the United Statesbefore routine infant hepatitis B vaccination was recommended. Althoughthese infections represented few of all HBV infections in the UnitedStates, it is estimated that 18 out of 100 people with chronic HBVinfection in the United States acquired their infection during earlychildhood. Clearly, infections occur among unvaccinated infants born tomothers who are not HBV-infected. In addition, unvaccinated foreign-bornchildren account for a high proportion of infections. More effort needsto be placed on vaccinating these unprotected children.

Hepatitis B vaccine, usually a three-dose series, is recommended for allchildren 0-18 years of age. It is recommended for infants beginning atbirth in the hospital. All older children who did not get all therecommended doses of hepatitis B vaccine as an infant should completetheir vaccine series as soon as possible. Most states require hepatitisB vaccine for school entry. Adolescents who are just starting theirseries will need two or three doses, depending on their age and thebrand of vaccine used. Adults at increased risk of acquiring HBVinfection should also be vaccinated. In addition, the vaccine can begiven to any person who desires protection from hepatitis B.

Groups of adults at increased risk of HBV infection

-   -   Healthcare workers and public safety workers with reasonably        anticipated risk for exposure to blood or blood-contaminated        body fluids    -   Men who have sex with men    -   Sexually active people who are not in long-term, mutually        monogamous relationships    -   People seeking evaluation or treatment for a sexually        transmitted disease    -   Current or recent injection drug users    -   Inmates of long-term correctional facilities    -   People with end-stage kidney disease, including predialysis,        hemodialysis, peritoneal dialysis, and home dialysis patients    -   Staff and residents of institutions or group homes for the        developmentally challenged    -   Household members and sex partners of people with chronic HBV        infection    -   Susceptible (non-infected) people from United States populations        known to previously or currently have high rates of childhood        HBV infection, including Alaska Natives, Pacific Islanders, and        immigrants or refugees from countries with intermediate or high        rates of chronic HBV infection.    -   International travelers to regions with high or intermediate        rates of HBV infection.

In addition, any adult who wishes to be protected from HBV infectionshould be vaccinated without having to acknowledge a specific riskfactor.

Exemplary Rules for Hepatitis C Virus (HCV) Testing

Testing protocol begins with screening patient sera for the presence ofantibody to HCV.

POSITIVE If the patient tests positive for anti-HCV with a ratio ofoptical density of sample signal to optical density of cutoff signal(S/C ratio) between 1.0 and 3.0, a confirmatory test is done to rule outa false positive anti-HCV result. Confirmatory tests are usually bothperformed (either PCR and LIA as in the CIP study, or PCR and RIBA). Ifthe PCR result is positive, then positive anti-HCV will be considered atrue positive. If both the confirmatory tests are negative, the antibodyresult is considered to be a false positive. If the RIBA isindeterminate and the PCR is negative, then the interpretation of thepositive anti-HCV result is uncertain. It could be a false positive, orthe person could be chronically infected with HCV or in the process ofseroconversion.

All patients who are found to have no or questionable evidence of pastexposure to HCV (FIG. 3) will be retested the following year.

NEGATIVE If the anti-HCV test is negative, PCR testing for the presenceof HCV RNA is conducted to rule out a false negative anti-HCV test. Ifthe PCR test is positive, the patient is considered HCV infected and nofurther testing need be performed. If the PCR result is negative, thepatient will be retested the following year.

FIG. 21D-38 depicts an exemplary algorithm for Hepatitis A Virus (HAV)testing, FIG. 21D-39 depicts an exemplary algorithm for Hepatitis BVirus Testing, and FIG. 21D-40 depicts an exemplary algorithm forHepatitis C Virus (HCV) Testing, according to an exemplary embodiment ofthe present invention.

I. Interpretation of Certain Results of Automated Data Mining Heat MapCorrelations Parasitic Worms and Hepatitis C

The various automated data mining protocols described above (and whosecomplete code is provided in Appendix D), can, for example, createpopulation heat maps from the exemplary CIP database. These populationheat maps may show positive, negative or no correlation between andamong various assays (or markers) or between and among assays and othervariables within the CIP population. For example, the population heatmaps revealed a correlation between antibody reactivity to parasiticworms and hepatitis C viruses. To explain this an exemplary systemcould, for example, automatically consult an internal hypothesisdatabase to search for possible explanations for the correlation. As canbe seen from the CIP hypothesis database described above and provided inAppendix B, none of the parasitic worm references discuss hepatitis andnone of the hepatitis C references discuss parasitic worms. Thus, inexemplary embodiments of the present invention, the exemplary systemcould, for example, then launch an internet search and access variousinternet databases, such as, for example, PubMed, MedLine, ScienceDirect, and NIH, as well as the Internet in general, to find anyinformation that might offer an explanation regarding the observedcorrelation. In the present example, a search of scientific articlesavailable in the PubMed database provided a basis for this observedworms-hepatitis C correlation as described below.

Hepatitis C virus (HCV) infection is the main cause of chronic liverdisease in Egypt and is largely associated with schistosomiasis.Concomitant infection with HCV and schistosomes can cause aggravation ofliver damage. These two infectious agents have been shown to havesimilar adverse effects on the immune system, as manifested by theiraction on cytokine production by Th1 and Th2 cells. Patients coinfectedwith hepatitis C virus and Schistosoma mansoni show high incidence ofviral persistence and accelerated fibrosis. It is possible thatenhancement of a Th2 response in co-infected individuals plays a role inpersistence and severity of HCV infection in patients concomitantlyinfected with S. mansoni.

Patients infected with schistomsoma frequently show a highseroprevalence of anti-hepatitis C virus (anti-HCV) antibodies. Theexact underlying mechanism by which schistosomiasis enhances HCVseropositivity is unknown. In Egypt, evidence suggests that individualsover the age of 40 have been exposed more to the risk of HCV infectionthrough inadequately sterilized needles used in mass anti-schistosomaltreatment campaigns conducted from the 1960s through the 1980s. Someresearchers have postulated that patients designated low positive forHCV antibody perhaps were falsely positive due to the generation ofautoantibodies in connection with Schistosoma mansoni infection.

A striking clinical feature of HCV infection is that more than 50% ofpatients with acute HCV develop chronic infection. It has been notedthat activation of Th2 responses seems to play a role in the developmentof chronicity in these patients. However, the possibility of helminth ornematode co-infection in these patients was not examined. Anotherresearch group investigating the possible diagnostic role of IL-10measurement, postulated that elevated IL-10 correlated in HCV-positiveschistosomal patients with the development of morbidity. Co-infectedindividuals appear to have increased Th2-related cytokines, andschistosomiasis may down regulate the normal stimulatory effect that HCVinfection would have on Th1 cytokines, leading to the chronicity of HCVinfection and playing a role in unresponsiveness to interferon therapyin co-infected patients. The same group of researchers found that HCVinfection correlated with an alteration of serum immunoglobulins inpatients with chronic liver disease.

A murine model of schistosoma/hepatitis co-infection demonstrated thatsuppression of the antiviral type I interferon response by schistosomeegg Ags in vivo predisposed the liver to enhanced viral replication withensuing immunopathological consequences. It is though that this modelmight be paralleled in human schistosome/hepatotropic virusco-infections, including hepatitis B and hepatitis C viruses.

During the time of egg deposition, schistosome-infected mice exhibit adown regulation of interleukin 2 and gamma interferon production towardparasitic antigens, mitogens, and foreign non-parasite protein antigens.A group of researchers found that mice infected with virus alone rapidlycleared the virus, while in animals co-infected with virus and S.mansoni, viral clearance was delayed by as much as 3 weeks in the liverand by several days in the spleen and lungs. These observations suggestthat helminth infection may influence immune responses to concurrentviral infections.

A cohort study conducted in the Philippines found that males infectedwith schistosomes consistently produced higher levels of Th2 cytokines,and also had a higher prevalence of liver fibrosis.

A case-controlled study was recently undertaken to describe theprevalence of Strongyloides stercoralis infection among patients withautoimmune liver disease, such as primary biliary cirrhosis, autoimmunehepatitis, and primary sclerosing cholangitis. The authors of that studyhypothesized that immunomodulation by S. stercoralis infection may lowerthe incidence of autoimmune liver disease.

Strongyloides stercoralis infection has been shown to be related toincreased risk in alcoholic cirrhosis. The same study found no increasedrisk for non-alcoholic cirrhosis.

Measles and Hepatitis

In another example, the population heat maps revealed a correlationbetween antibody reactivity for measles and hepatitis viruses. Theexemplary system first would automatically consult the internal CIPhypothesis database to search for possible explanations for thecorrelation. As can be seen from the CIP hypothesis database describedin Appendix B, none of the measles references discuss hepatitis and noneof the hepatitis references discuss measles. In exemplary embodiments ofthe present invention, the exemplary system would then launch aninternet search and access various internet databases, such as, forexample, PubMed, MedLine, Science Direct, and NIH, as well as theinternet in general, to find any information that might offer anexplanation regarding the observed correlation. In the present example,a search of scientific articles available in the PubMed database,provided a basis for this observed correlation as described below.

The hepatotropic viruses, measles and herpes viruses have been shown toact presumably as a trigger in patients with autoimmune hepatitis.

Adult syncytial giant cell hepatitis (GCH) is an uncommon and oftenfulminant form of hepatitis that may be caused by infection with a novelparamyxo-like virus. In situ hybridization studies showed that thedisease agent was genetically related to the measles virus. One group ofresearchers concluded that paramyxoviruses should be considered inpatients with severe sporadic hepatitis.

Epstein-Barr virus has a seroprevalence of more than 80% worldwide andis known to be associated with hepatitis. However, little is known aboutthe underlying pathogenesis and immune system mechanisms and there areno standard diagnostic criteria for diagnosing EBV-hepatitis available.

Viral infections of the mesenteric microvascular endothelium have beenhypothesized as pathogenic factors in inflammatory bowel disease. Thedetection of anti-measles virus IgM in the majority of patients withCrohn's disease and in about one-half of ulcerative colitis patients ascompared to a very low prevalence in patients with other chronicinflammatory disease is consistent with the hypothesis that the measlesvirus has pathogenic implications in inflammatory bowel diseases.

J. Extension of Database and Automatic Data Mining Functionality

In exemplary embodiments of the present invention, the database could beaugmented by utilizing electronic medical records, such as Google'sPersonal Health Record, for example, to supply the non-assayinformation. In such embodiments, an individuals health records could beautomatically downloaded to his database record each time they areupdated.

In exemplary embodiments according to the present invention, a databaseand analysis system can be fully integrated with any computer systemused to perform any of the applications described below in Section III,supplying, as it were, the back office number crunching conclusion to beoperated upon by any scoring, decision making, or other applicationsystem or software as may be useful.

Additionally, using the correlation matrix and data mining techniquesdescribed above, algorithms designed to operate on images, such as, forexample, pattern recognition algorithms and other image processingalgorithms, such as, for example, edge detection and morphology, couldbe used to refine correlation regions in a “variable space” toautomatically further process the data to find correlations of interest,as well as to find the precise segmentation in said variable space tomaximize the value of the correlation and thus find the group ofindividuals that has the most in most common as to that correlation. Byrepeating this process for every identified correlation and comparingthe results, i.e., the different optimal segmentations of the databasefor each correlation, the most can be learned about what is driving thecorrelation.

Finally, derived variables, such as the rate of change of antibodylevels through time, the ratio of various bioassay results, etc., can beadded to the database and correlations identified with respect to thesederived “second order” variables. It may be that a connection orphenomenon only manifests at the level of such secondary, or tertiary orn-ary variables, and only an automated process that methodicallyprocesses the data over and over with complex algorithms can bring tolight all the information buried therein.

In exemplary embodiments of the present invention, the time rate ofchange of an assay variable over time, as well as the second derivativewith respect to time of that assay variable. Algorithms can easily becrafted that track any changes in the assay values over time, as well asthe second derivative of such assays with respect to time. This can beespecially useful in analyzing cytokine data, which tends to fluctuatewith inflammations, colds and flu, but which often exhibits a baselinebalance between Th1 and Th2 categories, for example.

K. Exemplary Analyses Performed on CIP Database

In exemplary embodiments of the present invention, a sample can be firstanalyzed by assaying all or a plurality of cytokines, and then, based onthe individual's “cytokine signature” automatically spawning anadditional panel or superpanel of assays to perform. Such an exemplaryembodiment utilizes the cytokine's systemic immunological qualities topredict or locate potential areas for further study. Such a predictivealgorithm can be based, for example, upon where an individual's cytokinesignature lies within the Th1-Th2-Th17-Treg two dimensional space shownn FIG. 5D. For example, an individual with a high Th1 and high Th17profile could have his serum automatically tested for autoimmunemarkers, or for example, antibodies to bacterial infections. Anotherindividual with a high Th2 and high Th17 profile could have his serumautomatically tested for markers of allergy or atopic disease. Yetanother individual with high Th1 and Treg profile could automatically betested for a chronic mycobacterial infection, while another individualwith high Th2 and Treg could be tested automatically for parasiticinfection.

FIG. 21E-1 shows the interpretation of immunoassay results of a randomsampling of the Canadian Immigrant Population (CIP) database by PipelinePilot, categorizing the results as either reactive (positive),non-reactive (negative), or equivocal (marginal results interpreted asneither positive nor negative). Significant percentages (18-20% oftotal) of the results of two of the immunoassays for parasites from thepanel (filaria and strongyloides) were classified as equivocal, possiblyindicating a nascent infection in these individuals with theseparasites.

FIG. 21E-2 shows several signature Th2 cytokine assays compared withresults from parasite antibody assays. For filaria (FIG. 21E-2 a), thehighest amount of reactive results for both IL-4 and IL-5 occur in thefilaria equivocal patients. The same is true for IL-5 assay results forstrongyloides equivocal patients (FIG. 21E-2 b). If we are to logicallyassume that equivocal results for these assays were to become positiveresults in the near future for these patients, the increased levels ofIL-4 and IL-5 as signature Th2 cytokines may be indicative of earlystages of infection with both of these parasites. It is conceivable thatas the ImmunoScore database grows, results such as this might be seen asindicative of early intervention against parasitic infection in areaswhere such infections are endemic. A patient with an equivocal filariaresult might not ordinarily be treated, but elevated Th2 cytokine levelsin individual patients might call for treatment in patients that areequivocal for filaria and have elevated levels of both IL-4 and IL-5,for instance. In strongyloides patients, the initial flare of Th2cytokines seems counterbalanced by increased expression of inflammatorycytokines, IL-6 and TNF-α as shown in FIG. 21E-2 c. An increasedpercentage of patient population positive for these cytokines is seen insamples that are not negative (e.g. reactive and equivocal strongyloidesassay results).

Patients with serum antibody reactive to Hepatitis B core antigen areconsidered to have an active hepatitis B infection. This patientpopulation had higher levels of inflammatory cytokine TNF-α than didpatients non-reactive to core antigen (FIG. 21E-3). Seropositivity tocytomegalovirus (CMV) has been linked to an aging immune system, andinability to deal with debilitating infection in the elderly (e.g.influenza and Streptococcus pneumoniae). FIG. 21E-4 shows the CIPdatabase examined for seropositivity to CMV, drawing a distinctionbetween very high seropositivity (>250) and lower serum antibody levels.It can be observed that females are more likely than males to have veryhigh serum antibody levels to CMV, likely due to more interactions withvery small children. Examination of the region of origin of the CIPdraws clear distinctions in this population. Individuals fromsub-Saharan Africa, Southern Asia, and North Africa are more likely thannot to have very high levels of serum antibody to CMV, while individualsfrom the Latin America/Caribbean region, Eastern Europe, or SoutheastAsia are more likely to have fewer serum antibodies to CMV. Examinationof the age of the total population shows a clear increase in CMVseropositivity correlated with advancing age.

FIG. 21E-5 examines the trend for increased serum cytokine levelsplotted against CMV reactivity. The general trend for six of the eightcytokines examined is for increased serum levels of cytokines correlatedwith increased levels of CMV. One notable significant exception in thispopulation is observed in the levels of IFN-γ, which are inverselycorrelated with CMV seropositivity. It has been noted that elderlyindividuals are more prone to viral infection, and decreased levels ofIFN-γ in these individuals could be highly significant.

Serum cytokine levels were similarly examined vs. percent of CIP thathad serum antibodies to filaria (FIG. 21E-6). These results indicatedhigher levels of IL-4, IL-10, TNF-α, IL-17, and TNF-β to be associatedwith filarial infection, with no notable increases in the othercytokines. The increased level of IL-4 might be expected based uponreports of parasitic infections being correlated with overall increasein Th2 cytokines. IL-10 is associated with Treg cells and could also beexpected as a regulatory factor in dampening the Th2 response. Theincreased levels of TNF-α, TNF-β, and IL-17 could be indicative ofinflammation and could also be a cause for the increased levels ofIL-10. It is interesting that the apparent patterns of IL-17 and IL-23are somewhat incongruous, seeing that it is reported that IL-23expression is necessary to maintain a TH17 response. Other cytokines,reported to be pro-inflammatory, such as IL-8, IL-1, and IL-15 are notremarkable in their pattern of expression as related to filarialinfection. FIG. 21E-7 shows a decrease in IFN-γ levels in individualsseropositive for hepatitis A. This is somewhat unexpected in that IFN-γis considered crucial for the combat of viral disease. Similar resultswere seen with individual positive for hepatitis B core antigen (datanot shown).

An interesting cytokine profile is presented in FIG. 21E-8 ofindividuals possessing serum antibodies to Strongyloides. Theseindividuals show decreasing levels of TNF-α, TNF-β, IL-6, IL-17, IL-15,IL-8, IL-2, and IL-5, with a concomitant increase in IFN-γ, IL-23, IL-10and IL-4 levels. The IL-4 increase could be due to increased Th2expression, but this leaves the decrease in IL-5 expression moredifficult to explain. The IL-10 increase with the concomitant decreasein many other cytokines could possibly be a demonstration of thesuppressive effects of Treg cells expressing increased amounts of IL-10.Many of the pro-inflammatory cytokines are decreasing correspondent toStrongyloides antibody positivity, but there is an increase in IFN-γ andIL-23. Clearly the pattern of cytokine expression is complicated andworthy of further study. FIG. 21E-9 shows a multi-variate analysis ofcomponents of the CIP database incorporating the IL-6:IL-2 cytokineratio vs. serum levels of anti-CMV antibody (top panel) andanti-hepatitis B antibody (bottom panel) and color coded by age. Thisparticular representation is not necessarily informative, but ratherdemonstrative of the types of analyses that can be accomplished byImmunoScore technology.

CMV Reactivity/Non-Reactivity

FIG. 21E-11, Table 1, shows the percentages of the CIP that are positivefor at least one cytokine in the four cytokine categories previouslydescribed —Th1, Th2, Treg, and Th17, and analyzes this positivity inrelationship to positive serological tests for disease-specificantibodies. The mean values of the entire population are indicated ingreen, and significant deviations from the mean of the entire populationgreater than 1.0 are indicated in yellow. Thus, FIG. 21E-11, Table 1,shows that patients that are non-reactive for CMV antibody havesignificantly lower levels of Th1, Th2, and Th17 cytokines than dosubjects that are seropositive for anti-CMV antibody. Alsosignificantly, these same patients that are non-reactive to CMV havehigher levels of Treg cytokines. Taken together, these results indicatethat these patients are far less likely to suffer from chronicinflammatory conditions based upon their cytokine profiles. Patientsthat are seropositive or CMV antibody show the opposite—that is, theyhave elevated levels of TH1, Th2, and Th17 cytokines and reduced levelsof Treg cytokines. Due to the large number of CMV positive individuals(94% of CIP database), however, these results do not rise to the levelof significance.

Hygiene Hypothesis Revisited

The hygiene hypothesis has described a state wherein parasiticinfections in third world countries help prevent patients fromatopic/allergic conditions like asthma by tipping the Th1/Th2 balancetoward a more Th2-like state in those individuals. According to thehypothesis, we would expect to see a relative boost in the levels of Th2cytokines in patients that are seropostive for infections with filaria,strongyloides and schistosoma. The results in FIG. 21E-11 indicate thatwhile there are increases in levels of Th2 cytokines for patientsinfected with strongyloides and filaria, those patients with filarialinfections do not rise to the level of significance. Curiously, patientsinfected with schistosoma actually show a decrease in the level of Th2cytokines that also does not rise to the level of significance. Alsocounter to the stated hygiene hypothesis, the levels of Th1 cytokinesare increased in patients seropostive for strongyloides (significantly)and filaria. Levels of Th17 cytokines are increased in patientsseropositive for filaria (significantly) and strongyloides. Perhaps themost striking of the observations is that all three parasitic infectionsshowed increases in the levels of Treg cytokines, with patientsseropositive for filaria and strongyloides showing significantly higherincreases than the rest of the CIP database. It is likely that the storybehind the hygiene hypothesis is more complicated that a shift inTh1/Th2 cytokine balance and needs to consider the contributions of Th17and Treg cytokine-producing cells.

Mining the CIP Database

The Canadian Immigrant Population Database is organized by largegeographic regions of origin of the subjects. These regions are definedas follows:

-   -   Region 1=Sub-Saharan Africa    -   Region 2=South Asia    -   Region 3=North Africa    -   Region 4=Latin America/Caribbean    -   Region 5=Eastern Europe    -   Region 6=Southeast Asia

The Data Mining Tool was used to examine cytokine levels together withantibody levels to vaccine-preventable diseases, three parasiticinfections (filaria, schistosoma, and strongyloides), viral hepatitisinfections (A, B, and C), and infection with cytomegalovirus. Patientswere grouped according to region of origin and gender, and place in binsbased upon their ages, separated by 20 year increments. The agecategories in this instance were from 10-30 years of age, 30 through 50,50 through 70, and 70 through 90. The tool generated 39 separate heatmaps (FIGS. 21E-10.1 through 21E-10.21) based upon theseclassifications. The heat maps produced by the data mining tool displayvery interesting patterns of correlation based upon region of origin,gender, and age. Heat maps produced by population analyses withinadequate sample size produce very predictable, and uninteresting heatmaps. These would include those heat maps in FIGS. 21E-10.3 (top),21E-10.4, 21E-10.8, 21E-10.12 (top), 21E-10.15, 21E-10.18, and21E-10.21.

Individuals in the CIP database originating from Sub-Saharan Africa areshown in FIGS. 21E-10.1 through 21E-10.4. Discounting those patients inFIG. 21E-10.4 due to small sample size, the other individuals show veryinteresting patterns of correlation between cytokine expression andantibodies to the various antigens tested. In the female populationunder 30 years of age, cytokine patterns are largely inverselycorrelated with antibody reactivity to measles, hepatitis A, andhepatitis B core antigen. Male subjects in this same age category showsome negative correlations with some cytokines and varicella, and aseparate group of cytokines and antibody to hepatitis B core antigen. Asthe population ages, these heat maps change dramatically (FIGS. 21E-10.2and 21E-10.3). By the time the males reach middle age and older (50-70),there is a striking inverse correlation between all cytokine levels andparasitic exposure (e.g. filaria, schistosoma, and strongyloides shownin the bottom panel of FIG. 21E-10.3). This same group of individualshas a high degree of correlation among the parasitic infectionsthemselves (red grouping at very bottom right of the bottom panel ofFIG. 21E-10.3). In addition, there are interesting negative correlationpatterns among the cytokine expression in this group itself—inparticular, IL-8 and IL-5 expression are inversely correlated with anumber of the other cytokines. Curiously, IL-8 (a pro-inflammatorycytokine) is inversely correlated with a number of other inflammatorycytokines (IL-1. IL-17 and IL-23), as well as anti-inflammatorycytokines IL-10, IL-4, and IL-5. Individuals in the CIP databaseoriginating from southern Asia display very different heat maps (FIGS.21E-10.5 through 21E-10.7). The gender differences in heat map profilesas the population ages are striking. Isolating on the upper leftquadrants (or “cytokine quadrant”) in the six heat maps, one can seethat the younger males (under age 50) have better correlation values forthe cytokine expression in general than do the females. From the totalpopulation analysis, it is not certain which of the genders would beconsidered more fit immunologically, but the differences are striking.Both of the older groups (FIG. 21E-10.7) show many areas of negativecorrelation among the cytokine quadrant, particularly the females. Atthis point, it is unclear what the uncoupling of cytokine correlationsmean for an aging population. Further study and analyses may yieldimportant information regarding aging immune systems. It is possiblethat the uncoupling of cytokine correlations is a sign of immune healthin this population—there is a wealth of information that could possiblybe gleaned from ImmunoScore population analyses.

Regional geographic differences in population immune profiling can beseen when analyzing those individuals in the CIP database originatingfrom North Africa (FIGS. 21E-10.9 through 21E-10.11). The data in FIG.21E-10.11 is likely not as reliable due to small sample size, but theyounger individuals show striking cytokine profiles. The youngerindividuals (FIG. 21E-10.9) are remarkable for their lack of overallpositive cytokine correlation. Again, this may or may not be a sign ofdecreased immune health, but the profile changes in the olderindividuals in this population. Particularly in the males, there is muchmore positive correlation in the cytokine quadrant than in the youngergroup. The females from North Africa show very minor positivecorrelations in the cytokine quadrant when compared to other regionsaround the globe. This becomes obvious when examining the femalepopulations from Latin America/Caribbean (FIGS. 21E-10.12 through21E-10.14). The females in this group have striking positivecorrelations in the cytokine quadrant particularly in the 10-30 and30-50 age groups, but the positive correlations are still prevalent inthe 50-70 age group. In addition to the across the board positivecorrelation for cytokine values, there is a very noticeable negativecorrelation among cytokine values and hepatitis A infection in 30-50year old Latin American females (FIG. 21E-10.13—top). The younger LatinAmerican males show some interesting negative correlations betweencytokine values and filaria infection, and to a lesser extent, rubella,hepatitis A, measles, tetanus, and varicella (FIG. 21E-10.12—bottom).

The demographic groups from Eastern Europe and Southeast Asia weresmaller, but interesting nonetheless. In the younger groups from EasternEurope (FIG. 21E-10.16), there were interesting negative correlationsbetween the cytokine assays and the CMV antibody assay. CMV infection isnot as prevalent in Eastern Europe as some other areas of the world, andit would be interesting to investigate this relationship further,particularly in the light of the role CMV infection plays in immunesenescence. Hepatitis A and rubella also appear to have significantareas of negative correlation with cytokine expression profiles in thesegroups. Finally, in the group from Southeast Asia, the younger males(FIG. 21E-10.19—bottom) showed an interesting pattern of reactivitybetween the cytokine assays and the hepatitis B_e antigen assays. Therewere areas of strong positive correlation (e.g. IL-1, IL-4, IL-5, IL-6,and IL-17) and also areas of strong negative correlation (IL-2, IL-8,and IL-23). There is an interesting un-coupling of the Th17 cytokines inthis instance, namely the pattern of IL-23 is vastly different from thatof IL-17 and IL-6.

L. Exemplary Results Using Data Mining Protocols on CIP Database

Using the various automated analysis and data mining protocols describedabove, various analyses of the CIP database were performed. In a firstset of analyses, a series of cytokine based searches were run, using alibrary of cytokine analysis protocols that were created for thispurpose. These include the cytokine analyses described in the previoussection, as well as the creation of predictive models, based on bayesianmodeling, that take as inputs a plurality of cytokine assay values andout put a prediction for various non-cytokine immunological virus,parasite or other exposures.

FIGS. 21F-1 through 21F-6 depict the results of predictive models builtusing cytokine data according to an exemplary embodiment of the presentinvention;

FIGS. 21G-1 through 21G-12 depict the results of running an exemplarypatient population rule mining protocol according to an exemplaryembodiment of the present invention;

In exemplary embodiments of the present invention, a first assay panelcontaining a plurality of cytokine assays can be administered and theresults processed. Based on automatic analyses of the cytokine data, asecond tier or set of assays can then be run on the same individual. Thecytokine assay results being used to inform the contents of a secondassay panel. In this manner, it is not necessary to fund the assay ofentire superpanels unless and until the expense is justified. Thisexemplary embodiment can be particularly useful in a health caremanagement or insurance embodiment, as described below, especially whereassay costs are high or must be carefully controlled. The cytokine datacan be analyzed in a variety of ways, and a “cytokine signature” can begenerated and stored in the database. Such a cytokine signature can thenbe an input to a series of algorithms, the output of the totality ofwhich is a set of secondary assays to be run for that individual (orpopulation). The secondary assays and the cytokines can then together beprocessed, for example, in any of the ways described or disclosed above,or in the code provided herewith in Appendix D.

Also very useful in an insurance underwriting or health care managementapplication, is the exemplary single patient vaccine recommendationprotocol. Although originally named for its vaccine recommendationcapabilities, it actually tallys all of an individual's assay results,calculates and presents where within the database they fall percentagewise, and displays therapeutic, diagnostic and other recommendations orobservations based on the assay and other variable values in thatindividuals' record.

Such a protocol can be used, for example, to calculate an immunoscore,an overall immune status, with one or more sub-immunoscores, and anunderwriter can use such a protocol from their desktop computer toimmediately look up the electronic file on any patient or insured, andcan approve health care procedures, intelligently audit for premium orrating purposes, and generally have command of the individual's entirehealth picture at a glance, all assisted by the system intelligence.

FIGS. 21H-1 through 21H-10 depict the results of running an exemplaryindividual patient vaccine recommendation protocol according to anexemplary embodiment of the present invention for 10 randomly selectedindividuals' within the database.

Exemplary Automated Data Mining Complete Output

FIG. 21I is an exemplary output from an exemplary automated data miningprotocol according to an exemplary embodiment of the present invention,segmenting an exemplary database by Region of origin, Sex and thecytokine assay IFN-gamma. The output includes

Uses of Immunoscore Information and Automated Data Mining Results inVarious Commercial, Research and Governmental Contexts

In exemplary embodiments of the present invention, ImmunoScoreinformation (including, for example, results of assay panels, individualhistory and records of health care visits and treatments administered orundergone) processed in an exemplary system and stored in an exemplarydatabase can be used in a variety of commercial, research andgovernmental applications. These uses can range from optimizing thehealth care costs of a medical insurance underwriter to facilitatingimmunogenicity studies for a pharmaceutical manufacturer, or, forexample, to tracking the incoming and subsequent immune status ofimmigrants. In what follows, descriptions of several exemplary businessmethods which leverage or exploit the use of ImmunoScore informatics arepresented.

A. Health Insurance Underwriting and Management

In exemplary embodiments of the present invention, systems and methodsaccording to the present invention can be used, for example, to optimizethe business of health insurers as well as healthcare providers, who areessentially self insurers. In general, a health insurance underwriter ora health insurance provider has a population of individuals, generallycalled insureds or plan members, whose medical care costs are reimbursedor paid for directly by the healthcare insurer or the healthcare plan.In such contexts, it is useful to monitor the health of the populationof insureds or plan members, especially those who are older and in thoseyears, generally, for example, starting at age 60, when individualsbegin to encounter greater health and medical problems.

In exemplary embodiments of the present invention, each plan member orinsured, or, for example, each plan member or insured above a certainage, can be assayed, and the results can be used to determine whetherany prophylactic therapy should be administered to these individuals.Sometimes the decision is as simple as identifying vaccine preventablediseases for which the individual does not have sufficient levels ofantibodies. In that case, the prophylactic therapy would be theadministration of the vaccine in question. More complicated decisionscould include identification of diseases or of biochemical markerstherefor, that an insured or plan member is susceptible to that do nothave a direct and economical prophylactic therapy. In that case, therecan be, for example, a more complex algorithm which decides what to dogiven (i) assay results and (ii) the relative costs of assuming the riskthat the insured will contract the disease versus the costs ofprophylactic therapies to prevent the disease or diseases implicated.Such algorithms could, for example, be implemented in a system such asis depicted in FIG. 2A, where, for example, in addition to database 203where the results of assays conducted on individuals are stored, therecan also be a business rules database 220 which can also supply inputsto a central processor 204 which implements such analysis andalgorithms. The inputs to such algorithms can then be, for example, notjust assay results, medical history and demographic information, butalso a set of business rules allowing a decision to be made orfacilitated, taking into account the relative costs and benefits ofadministering prophylactic therapies. Such benefits to be consideredcan, for example, be those inuring to the individual as well as thoseinuring to the members of the health care plan as a whole, or, thosewhich seek to maximize profits or efficiencies. In exemplary embodimentsof the present invention such a healthcare insurance optimization methodcould be implemented as is illustrated in process flow diagrams FIGS. 22and 23.

As can be envisioned from the CIP database, it appears that the level ofanti-Rubella antibody is uniformly lower in those individuals from SEAsia. Rubella is a generally a mild, self-resolving infection except inpregnant females, in which instance there are undue complications to thenewborn, known as Chronic Rubella Syndrome (CRS). In an immigrantpopulation such as the one documented in the CIP database, if women ofchild-bearing age from SE Asia were demonstrated to be susceptible toRubella infection, health care authorities, as well as thoseunderwriting insurance policies would benefit from such information. Notonly are those women more at risk during pregnancy, but this particularimmigrant population would be more likely to infect native Canadians ofchild-bearing age (assuming that their own antibody levels had waned).The general health of the population, therefore, would be well-servedmaking sure that these individuals were appropriately vaccinated toavoid Rubella infection and possible complications to child-bearingwomen. These data reveal that Canadian authorities (and by extension,those in the United States) could, for example, be well served andfiscally responsible in the long run by testing and immunizing theimmigrant population against Rubella and other vaccine-preventablediseases.

FIG. 22 depicts an exemplary process flow for a health care managementapplication. With reference thereto, at 2201 an insured's immune statuscan be examined, for example by conducting one or more assays or panelsof assays such as, for example, those that are described above. At 2202,for example, the results of those assays can be used to identifydiseases that the insured is susceptible to, and moreover, the risk ofcontraction of each disease for that individual can be calculated. At2203, prophylactic therapies that could prevent each identified diseasecan be identified, and at 2204, for each identified disease a decisioncan be made by calculating the expected costs of treatment (such as, forexample, by taking the known costs of treatment multiplied by theprobability of contraction) and the costs of associated prophylactictherapies. Finally, at 2205, prophylactic therapies that cost less thanthe expected costs of treatment can be required for the insured as acondition of maintaining his or her insurance coverage or membership inthe health plan. For those prophylactic therapies whose costs aregreater than expected treatment costs but nevertheless desired by theinsured, the cost differential can be born by the insured rather thanthe health insurer.

FIG. 23 depicts a particular subset of the process flow illustrated inFIG. 22 where the prophylactic therapies are simple and the ailmentsidentified are vaccine preventable diseases. Beginning at 2301, aninsured or plan member's immune status is examined by conducting one ormore assays or panels of assays such as those described above. At 2302,vaccine preventable diseases to which the insured is susceptible areidentified based on an analysis of the results of the immune status from2301. At 2303, the insured can be, for example, required to obtainvaccines for the identified vaccine preventable diseases. At 2304,follow-up examinations of the insured's immune status post-vaccinationcan be made, again by conducting one or more assays or panels of assays,and these results can also be stored in the database. At 2305, thefollow-up examination results can be used to evaluate the efficacy ofany administered vaccines to provide the necessary immunity to theidentified diseases for this individual. When extended to an entirepopulation, such as, for example, the insureds of a health insurancecompany or the members of a health plan, this can, for example, providea means of evaluating the efficacy of vaccines in an aging population.This can also be very useful in the context of measuring and dealingwith immunosenescense, as described below.

Next described are a number of process flow charts which illustrateexemplary process flow according to various embodiments of the presentinvention applied to healthcare management applications. FIG. 24 is analternative process flow to that depicted in FIG. 22, and is concernedwith adjusting an insurance premium or an HMO participation fee for anindividual based upon identification of potential diseases that anindividual is susceptible to using ImmunoScore diagnostics.

The context of FIG. 24 could arise, for example, where an insurancecompany or HMO requiring an annual ImmunoScore diagnostic panel as acondition of maintaining insurance coverage or participation under ahealthcare plan. Such annual requirement would be akin to the annualinformation questionnaires that automobile insurance companies requireof all of their insureds wherein an insured must state if he has had anyserious health problems, if he has been involved in any accidents, or ifother out of the ordinary events have occurred. With reference to FIG.24 at 2401, the individual's immune status can be examined and at 2402,based upon the results of such examination, all diseases to which theindividual is susceptible can be identified. 2405 is a decision treewhich is applied to each disease identified at 2402. Thus, at 2405, foreach disease a decision is made as to whether a prophylactic therapy isavailable. If there is no such available therapy, the flow terminates at2410 where the insured's premium is adjusted upward, to account for theadditional risk the insurance company is taking in continuing to coverthis individual. If, at 2405 there is a prophylactic therapy availablethen the flow moves to 2406 where it is determined whether to administeror approve the prophylactic therapy. Based upon this decision, thepremium can also be adjusted.

FIG. 24A is a more detailed version of the analyses described inconnection with FIGS. 22 and 24. With reference to FIG. 24A, at 24A01the immune status of an individual can be examined, and at 24A02 theinitial total cost can be set to zero. 24A02 through 24A35 are thenapplied in a loop which cycles over all of the diseases for which anindividual is tested in the examination at 24A01. Such identifieddiseases can be, for example, those indicated by analyzing the resultsof assays conducted and other data associated with the individual orvarious populations to which he/she belongs, as described above. Foreach potential disease, at 24A05 it can be determined whether theindividual is susceptible or not based upon the assay results. If theindividual is not susceptible, process flow can terminate as to thatdisease at 24A20 and no incrementation of cost occurs. If the individualis susceptible, the flow moves to 24A10 where it is determined whether aprophylactic therapy exists. If a prophylactic therapy does not exist,at 24A30 the total cost is incremented by the cost of treatment. If sucha therapy does exist, at 24A05 it can be determined whether thetreatment cost from the disease is greater than the cost of theprophylactic therapy. If the treatment cost is greater than the cost ofsuch therapy, then at 24A35 the prophylactic therapy can be offered tobe reimbursed up to the treatment cost and the total cost can beincremented by the treatment cost. If the cost of prophylatic therapy isgreater than treatment cost, then at 24A25 the individual is required totake the prophylactic therapy and the total cost can be incremented bythe prophylactic therapy's cost. After looping through all of thepotentially relevant diseases, at 24A50 the premium can be adjustedbased upon the total cost. The computation of total cost andprophylactic therapy cost at both the disease specific level and theover-all levels can be given by the following rules:

Disease specific:

Computation of TC: P(CD|IS and not PT)*C(T|CD and not PT and IS)

Computation of PT Cost: P(CD|IS and PT)*C(T|CD and PT and IS)

Overall Disease-Related Healthcare Costs:

TC=ΣP(CDi|not PT and IS)*C(Ti|CDi and PT and IS)+C(PT) (in all diseases)

PT=ΣP(CDi|not PT and IS)*C(Ti|CDi and not PT and IS)

The various exemplary implementations of healthcare management describedabove have considered each disease individually. FIG. 25 addresses amore complicated situation where all of the potential diseases areidentified and all prophylactic therapies available for all of theidentified diseases are also identified in all possible combinations ofdiseases and prophylactic therapies are analyzed using a cost benefitapproach. Thus, with reference to FIG. 25, at 2501 a panel of assays canbe conducted. At 2502, based upon the results of such assays alldiseases the individuals are susceptible to are identified. At 2505 allprophylactic therapies which are available for each of the identifieddiseases can also be identified, and at 2510 a cost benefit analysis ofall possible combinations of prophylactic therapies and diseases can be,for example, undertaken using business rules. Implementation of thisfunctionality represents a much more complex level of analysis as it isnecessary to first define all possible combinations of diseases andprophylactic therapies. For example, if the individual is susceptible tofive diseases and a prophylactic therapy exists for each of them butthese prophylactic therapies vary widely in cost, it can be, forexample, useful to a healthcare manager or a healthcare insuranceunderwriter to know whether it may be more economical to only administersome of the identified prophylactic therapies and run the risk of theindividual contracting the diseases for which prophylactic therapies arenot administered. For each of the possible combinations a cost in termsof cost of administering the prophylactic therapy and expected cost oftreatment without the therapy is assessed and at 2515 one or moretherapies can be approved and/or the insured's premium or theindividual's insurance premium adjusted.

It is understood that in the description of the various possiblealgorithms which can be used in an ImmunoScore analysis for healthcaremanagement that the term individual, insured, and healthcare planparticipant are functionally equivalent. While some algorithms areexpressed in terms of health insurance context, the same analysisrepresented by them can easily be applied to HMO management ormanagement of other healthcare plans. As will be described below, thesame techniques can be applied where the entire population is coveredunder a healthcare plan, such as, for example, in a socialized medicinejurisdiction. Alternatively, the same techniques can be applied where alarge population of some mutual affinity is covered by a singlehealthcare plan such as, for example, United States Veterans whosehealthcare is provided by the U.S. Veterans Administration. Thus, it isunderstood that any particular algorithm or method described in onecontext also applies to any other contexts.

FIG. 25A is identical to FIG. 25 except that it offers an additionaloption. At 25A20, if, in fact, the minimum cost, which is simply thetotal cost of the least costly permutation at 25A10, is, for example,too great for underwriting limits or healthcare management criteria at25A20, the participant can, for example, be canceled from the plan.

FIG. 26 depicts an exemplary process flow for use in healthcaremanagement applications. FIG. 26 is not concerned with dollar costs butrather cost in terms of quality of life. Such an analysis would beuseful where dollar cost is less important than quality of life, suchas, for example, in exemplary embodiments where a supplemental insurancecompany insures a minimum quality of life and undertakes to provide forwhatever healthcare costs are necessary to maintain that quality oflife. Additionally, a socialized medicine jurisdiction, for example,could have a minimum quality of life which it seeks to provide to eachcitizen as a basic human right which that jurisdiction sees all of itscitizens as having. With reference to FIG. 26, at 2601, an immune statusof an individual can be examined and the quality of life can be set tozero. For the purposes of FIG. 26, a higher quality of life scoretranslates to a higher quality of life. At 2602 all diseases to whichthe individual is susceptible are identified and a decrease in QOL scorecan, for example, be assigned to each disease. The scoring data (i.e., amap of identified health scenarios to some QOL metric) can, for example,be stored in a business rules database such as is depicted in FIG. 2A.Such a decrease in quality of life score can be, for example, a measureof unexpected pain and suffering, a measure of how many sick days aregenerally associated with it, or, for example, whether the sick days areat home, taken at the hospital, or taken while still at work, andfinally whether surgery is involved. At 2605, all prophylactic therapieswhich are available for all of the identified diseases at 2602 can alsobe identified. At 2610 for each identified disease and each possiblecombination of identified diseases (assuming that the individual couldcontract more than one disease, either simultaneously or in succession)the probability of contracting the disease can be computed and from thatprobability an associated expected decrease in quality of life can be,for example, computed. As provided in FIG. 26, an exemplary formulawhich can be used in this context:

E(QOL _(DEC))=Prob(Disease)*ΔQOL;

QOL=QOL−E(QOL _(DEC))

At 2615 an increase in quality of life can be assessed for eachidentified disease or combination of identified diseases for whicheither prophylactic therapies or therapeutic therapies exist. Thus, inexemplary embodiments of the present invention, the quality of lifescore can be incremented by looping through each disease and adding theexpected increase in quality of life associated with either (i)providing a prophylactic therapy or (ii) a therapeutic measure tomitigate the loss and quality of life due to contracting the disease.For example, not every disease for which there is a prophylactic therapycan be totally obviated. Some diseases to which individuals aresusceptible can be mitigated but not prevented by prophylactictherapies. For example, when people feel the onset of a cold they oftentake echinacea. Echinacea tends to lower the amount of time one issymptomatic but rarely totally prevents contracting the cold.Alternatively, if a prophylactic therapy completely obviates theindividual from contracting the disease then the E(QOL_(inc)) shouldexactly equal the E(QOL_(dec)). If the prophylactic therapy happens, forexample, to bestow other benefits besides preventing the disease, thenthe expected increase in the QOL associated with undergoing theprophylactic therapy would exceed the E(QOL_(dec)). Similar computationswould apply to various possibilities. At the end of process flow in FIG.26 a net quality of life figure can thus be computed.

FIG. 26A is a more detailed process flow for the example illustrated inFIG. 26 with the exception that in FIG. 26 an improved QOL is indicatedby a more positive score and in FIG. 26A an improved QOL is indicated bya more negative score. At 26A01 immune status can be examined and at26A02 the quality of life can be set to zero. At 26A10 the probabilityof contracting a disease given the immune status obtained in at 26A01can, for example, be computed. At 26A20 the probability of contractingthe disease given the immune status can be multiplied by a “badness”score. At 26A30 this product can be added to the quality of life score.26A10 through 26A35 can then be repeated for each disease for whichsusceptibility could be examined, given the assays administered at26A01. In this exemplary process flow a better quality of life isassociated with a lower number which is the opposite convention of thatadopted in the process flow of FIG. 26. It is for this reason that a“badness” score is assigned to each disease and an expected “badness” isadded to the quality of life at 26A30. Additionally, at 26A15, allpossible prophylactic therapies for the identified disease (it is notedthat 26A15 and 26A35 are within the for-each-disease loop as well) canbe generated and mitigation scores can be assigned for each physicaltherapy or combination thereof. At 26A35, the mitigation score can be,for example, subtracted from the quality of life score and once flow islooped from 26A10 through 26A35 for each disease, at 26A40 a totalquality of life score can, for example, be output. Using this totalquality of life score, at 26A50 the best set of prophylactic therapiesin terms of higher quality of life can be offered to the individual withthe stated quality of life improvement.

It is noted that in the schema of FIG. 26A a badness score is associatedwith each contracted identified disease. An exemplary badness scoringsystem is presented in the upper right of FIG. 26A and comprises, forexample, +1 for a home sick day, +10 for a hospital sick day, +½ for awork sick day, and +100 for a surgery. Accordingly, the quality of lifescore would dramatically decrease if the individual was found tosusceptible to a number of diseases each of which required surgery ifcontracted.

FIG. 27 is a final healthcare management exemplary process flow chart.FIG. 27 addresses a newly discovered HPV vaccine that is 100% effectivein preventing cervical cancer in women. The question is who shouldreceive the vaccine and when should they be tested. From the point ofview of society as a whole, perhaps everybody who has not contracted HPVshould be vaccinated to prevent them from ever contracting it and thusprevent the females amongst them, and females in contact with the malesamongst them, from ever contracting cervical cancer. Of course, this hasa greater cost than simply vaccinating women prior to their exposure toHPV. Therefore, the decision as to who receives the HPV vaccine willoften depend upon who is managing the healthcare of the population inquestion. This will be described in connection with the final decisionat 2715.

With reference to FIG. 27, beginning at 2701, an assay panel containingan HPV assay can, for example, be conducted relative to one or moreindividuals. At 2705 it can be determined whether that individual isseronegative or seropositive to the HPV virus. If seronegative, theindividual has not yet contracted HPV and flow moves to 2710, where thedecision as to the individual's gender is made. If the individual is amale, is not seronegative, and is seropositive to HPV, then flow canterminate at 2706 and any therapeutic treatments that are available canbe administered. Continuing at 2710, if the individual is a female flowterminates at 2711 and the HPV vaccine is always administered. Whetherthe healthcare manager is an insurance company, an HMO, a socializedmedicine jurisdiction or a large scale healthcare management entity suchas the Veterans Administration, any female whose healthcare is beingmanaged should be vaccinated to prevent any healthcare expenditure intreatment for cervical cancer. However, what about males? The onlyutility derived from vaccinating males is that females in sexual contactwith them will not contract HPV. If those females are managed by adifferent healthcare entity there is little utility in protecting “our”men. If those females are protected in the same healthcare managemententity, then there is utility in protecting them. Alternatively, even ifthe females are not provided healthcare or healthcare insurance under agiven plan, a government regulating that plan may see a social benefitin wiping out cervical cancer, or at least those cervical cancers causedby HPV, which are the vast majority of such cancers. Accordingly, givenall of these concerns, at 2715, the HPV vaccine can be administered ifthe utility value of the prophylactic effect is greater than the cost oftreatment, which is simply the cost of the vaccine. The utility valuewill, as noted above, be a complicated function of a number of factors,the most prominent of which being who is responsible (financially,politically or morally) for the healthcare of the females that this malemay come in contact with.

B. Health Care/Health Insurance Credit Exchange

The applications that have been described thus far relating tohealthcare management all assume that in the cost benefit analysis,additional costs can be passed to an insured, or, for example, if toohigh, the insured or member of a health plan (such as an HMO) can becanceled. While this may maximize profits for the health plan or thehealth insurance company in the short run, it can result in dissatisfiedinsureds and eventually loss of a certain percentage of the insured baseof individuals. Loss of customers is never a good thing, even if undercertain analyses they are unprofitable customers. One way of solvingthis problem is, instead of passing costs through to consumers, i.e., toinsureds or health plan members, to set up a means by which they canprocure credits in years when they are predominately healthy and usethose credits when not costs but—debits—are assessed against them as perthe exemplary analyses described above in connection with FIGS. 22through 27. Thus, in exemplary embodiments of the present invention, ahealth care provider, a health care insurance company, or otherfinancial intermediary in conjunction with the health insurance provideror health care provider, such as an HMO, can set up a health insurancecredit exchange. Such an exchange can operate in a fashion similar tothose government programs which have rules against excessive energy useor excessive pollution derived from an entity's activities. An entitywhich is a polluter, or an “excessive” user of energy or a naturalresourse such as water, for example, can purchase credits from otherindividuals or entities who have a low energy use, low water use or arelow polluters. In this fashion, those individuals or entities who exceeda certain threshold of some desirable metric, such as, for example, lowenergy use, low water use, or other “green” factors, can purchase,negotiate, trade or otherwise procure credits from those who are belowsuch a threshold so as to avoid fines or negative consequences fromviolating the environmental or natural resource use standards.

Thus, in the health care context there are always some individuals whoare sick more than others. Individuals do not know whether they will bein the underwriting bin of more sickly than average or less sickly thanaverage. Insurance companies try to spread the risk of the more sicklyamongst a larger population which obviously includes those who are lesssickly, and charge an essentially average health insurance premium toeveryone. However, as underwriting becomes more granular, usingexemplary embodiments of the present invention, it can be predicted,even decades in advance whether a particular individual is more or lesslikely to contract a disease, such as for example, autoimmune diseasesas described above. For example, as described above in Section I,certain autoimmune diseases have markers which are harbingers 7-10 yearsin advance of their eventual symtomology. Thus, using exemplaryembodiments of the present invention, health care plans, health careadministrators and health care insurers will be able to divide thepopulation into many more bins of insureds and associate with each ofthem a more accurate health insurance premium cost. This can cause thosein the more risky bins to have a much greater insurance cost. One way ofameliorating this is to encourage people to join health care plans earlyin their lives when they are healthy and before even the onset ofeventual disease emerges, such as via a marker or predictor in anImmunoscore assay result marker context. In so doing, people who arehealthy can receive credits which they can bank within the system orbuy, sell or trade. If regular Immunoscore audits of individuals revealthat someone is moving from a less risky bin into a more risky bin, anda cost would be added to their health insurance premium (i.e., a debit),instead of paying an extra premium they can procure a credit through ahealth care credit exchange either from their own account which theybanked in earlier years or from other healthy peoples' accounts whichare presently available for exchange.

Thus, in exemplary embodiments of the present invention, an insurancecompany could, by setting up and maintaining such a healthcare creditexchange, retain more customers as well as encourage customers to joinits ranks of insureds early on in their lives so as to be able to bankfor the future and/or sell credits for being healthy. By acting asintermediary, an exemplary system can make a market for such health carecredits, and not have to wait for a particular debit holder to find aparticular credit holder willing to exchange. Acting in some ways as asecurities market maker, an Immunoscore based third party can buycredits and sell debits.

Thus, in exemplary embodiments of the present invention, insureds canthus be induced to pay higher premiums when they are younger and morehealthy which would therefore give them extra protection against beingassessed debits later on should they become sick. This results in a netflow of capital to the health insurers, or the HMOs, because they cancharge higher premiums than the “true” or correct “premium” with thefull consent of the insured in exchange for allowing and facilitatingparticipation in the health care credits exchange. On the other hand,they can also retain more customers because people who are subject todebits as a result of more granular analyses of their overall health viaImmunoscore diagnostics can simply use credits they have accumulatedearlier in their lives or procure credits from other insureds whichwould ultimately be cheaper for them than having to find substandardcoverage. Additionally, the insurance company is not faced withcanceling bad insureds and then having to spend client development moneyto procure new “good” insureds, rather, it can more or less retain itsinsured base as well as generate additional profits from the maintenanceof the healthcare credit exchange.

Further, if a healthcare management entity sets up a health care creditexchange it can, in exemplary embodiments of the present invention,require immunoscore diagnostics, such as set forth in Section 1 above,at various significant life points in each insured's lifetime. This canhave the effect of positive feedback in the amount of data that animmunoscore database has available and thus, an improvement and greateraccuracy and predictive value that the algorithms of the Immunoscoreanalysis can provide to the insurer. Over the course of time an insurerwill tend to make more money and have more accurate predictive modelsthan its competitors who do not use such an Immunoscore system.

Finally, ImmunoScore databases lend themselves to storing health carecredit and debit information as part of an individual's record, makingit nearly seamless to create algorithms to track such credits/debits andmanage the exchange. After all, ImmunoScore is the tool being used togenerate the very granularity that assigns the credits/debits and makesthe entire business possible.

C. Veterans Health Care Management (Variant of Health Care)

A special instance of health care management relates to veterans care.In the United States, the Veterans Health Administration (VHA) providesa broad spectrum of medical, surgical, and rehabilitative care to itscustomers. Individuals that qualify for veterans healthcare servicesinclude, for example, returning Active Duty, National Guard and Reserveservice members of Operation Enduring Freedom (OEF) and Operation IraqiFreedom (OIF). The vision statement of the VHA states that it needs tobe a comprehensive, integrated healthcare system that providesexcellence in health care value, excellence in service as defined by itscustomers, and excellence in education and research, and needs to be anorganization characterized by exceptional accountability and by being anemployer of choice.

In exemplary embodiments of the present invention, veterans, with theirspecial requirements based on service, can be well served by ImmunoScorediagnostics and data management. As previously described in Section I,soldiers have very specific vaccination requirements based on theirdeployment and area of expertise. ImmunoScore diagnostic panels can betailored to the needs and context of the individual soldier based uponhis or her previous exposure to immunization and also to differentinfectious agents depending on the relevant theater of deployment. Inaddition to immune response to infectious agents, veterans are likelycandidates for measurement of immune system perturbations induced by,for example, Post Traumatic Stress Disorder (PTSD), exposure to uniquechemical agents (e.g., Agent Orange), Gulf War Syndrome, and recoveryfrom injuries sustained in service.

As described above in connection with the CIP database, linearregression analysis of a patient database could yield valuableinformation pertinent to appropriate treatment of veterans after theiryears of service. Those analyses displayed possible correlationsbetween, for example, measles and mumps immunity and immunity tovaricella infection. Any possible associations between service localeand adverse agents could be documented and analyzed by an exemplaryImmunoScore data mining process in similar fashion.

The VA Research and Development program (The Office of Research andDevelopment) aspires to lead the Veterans Health Administration inproviding unequaled health care value to veterans. The ImmunoScoretechnology can help contain healthcare costs for veterans by monitoringand analyzing immunologic information.

D. Socialized Medicine Management

A socialized medicine jurisdiction is essentially a health care provideror insurer for an entire population. Thus, the health care managementapplications of ImmunoScore described above can also be implemented in asocialized medicine jurisdiction. Countries with socialized medicine,such as the UK, New Zealand, and particularly Canada, presentopportunities to stress preventive medicine for the good of the populace(i.e., by maximizing QOL for a given health care budget) and theadvantages of lower cost healthcare as represented by ImmunoScoremanaged healthcare. These governments could be provided with healthcaremanagement services via an implementation of the ImmunoScore system.

The CIP database discussed in Section II above, has revealed the utilityof an exemplary ImmunoScore database for a country with an immigrantpopulation. There has been much concern regarding outbreaks of mumps inthe United States and Europe. This disease has clearly been shown tospread from contact with travelers (CDC, 2006). The CIP databaseindicates a degree of relatedness between patients that have antibodiesto both Rubella and Mumps. If this type of analysis were to be extendedto geographic regions and associated with specific genders, a governmentthat supported socialized medicine could, for example, be very much infavor of assuring that an immigrant population was properly immunized,for the protection of that immigrant population, as well as the nativepopulation.

E. Supplemental Insurance (AFLAC Model)

AFLAC is the leading provider of supplemental insurance, which provideshelp with expenses not covered by an individual's major medical plan.The company is the number one provider of guaranteed-renewable insurancein the United States and Japan. Its products provide protection to morethan 40 million people and go beyond the traditional insurance bydirectly paying claimants with cash benefits.

With the cost of health care rising, the challenge for most employers isto satisfy the specialized needs of each employee without having to fundexpensive new plans. AFLAC provides products including, for example, thefollowing: Accident Disability; Short Term; Disability; Cancer Benefit;Hospital Indemnity.

ImmunoScore diagnostic testing and database storage can provideinformation for use in just such supplemental insurance programs.ImmunoScore can, for example, provide an individual with immune statustesting that could be monitored over time and offer the peace of mindthat would come from knowing that that patient had a “healthy” immunesystem. In addition, an insurer would be better able to underwritepremiums for supplemental health insurance with a sounder understandingof the patient's health status.

Additionally, in exemplary embodiments of the present invention, a“immunological insurance plan” could be offered. Such a plan couldprovide all immunological monitoring and therapeutics to each insuredfor a fixed annual premium and guarantee a certain defined quality oflife to each insured. Such a plan could utilize one or more of thehealth care management processes described above.

To be able to effectively underwrite such supplemental insurance,supplemental insurance firms need to be aware of relatedness betweenimmune parameters as revealed by database analyses. For instance, theCIP database revealed tendencies for Hepatitis A antibody to be presentin individuals from certain geographic regions. Supplemental insurancecoverage could benefit from insuring that travelers to these regionswere assured of their own immune system's ability to combat Hepatitis Ainfections in regions where the disease is endemic. Or, for example, theCIP database revealed a possible suspension of Tetanus immunity amongstindividuals reactive to CMV. In exemplary embodiments of the presentinvention, a health insurer (whether supplemental or primary) would takespecial care to take such a factor into account.

F. Immunoscore and the Wellness Industry

In 1994, the U.S. Congress laid the groundwork for the Wellness Industryby passing the Dietary Supplement Health and Education Act (DSHEA). ThisAct set new standards for the manufacturing, testing and marketing ofnutritional products. Products that meet strict government standardsearn the title of nutraceuticals. Blurring the line between conventionalfoods and drugs, nutraceuticals are defined as foods or parts of foodthat confer health or medicinal value, including the prevention andtreatment of disease.

The Food Policy Institute (http://www.foodpolicyinstitute.org) hasdefined drivers of nutraceutical industry growth. The nutraceuticalmarket was once viewed as largely a counter-culture “back to nature”phenomenon, but is now buoyed by a number of solid fundamentals.

Changing consumer demographics. Americans are living longer andemphasizing the importance of quality of life in their later years. Asthe baby boomers approach ages where personal health becomes moreparamount, the demand for mechanisms for conveying health will grow.

Increasing ethnic diversification. The mainstream U.S. nutraceuticalsindustry is a relatively new phenomenon. However, the use of foods,herbals, and other natural products to convey health and medicinalvalues has a long history of acceptance by many of the world's cultures.

Paradigm shift in personal health. Americans are taking moreresponsibility for their personal health, embracing the concept ofhealth maintenance and wellness. Thus, the paradigm is shifting awayfrom disease treatment and towards disease prevention.

Dissatisfaction with Western healthcare. Americans are becoming morereticent about accepting the side effects of synthetic drugs andremedies. Similarly, rising healthcare costs are encouraging Americansto explore alternatives to traditional orthodox medicine.

Increasing acceptance of alternative healthcare practices. There is agrowing acceptance among Americans of alternative or complementarytherapies and wellness modalities. Recent years have witnessed increaseduse, for example, of chiropractic care, vitamin therapy, aromatherapy,meditation and relaxation techniques, and acupuncture.

Increased understanding and awareness of diet-disease relationships.Many of the leading causes of premature death in the U.S. arediet-related. Examples include heart disease, diabetes, and many typesof cancer. The USDA estimates that diet-related disease and death coststhe U.S. in excess of $250 billion each year.

The Food Policy Institute has identified challenges facing thenutraceutical industry.

Few farmers are producing herbals and other botanical inputs (due tolimited market knowledge, technical requirements and other obstacles.

Limited access to finance and capital constrains industry developmentand expansion.

Ambiguous regulatory framework for ensuring product standardization andefficacy.

Regulatory restrictions on marketing products via health claims impederetail efforts.

Raw material supply issues (consistency of quality and availability) forbotanical manufacturers.

Limited endorsement by traditional healthcare practitioners.

Consumers can not differentiate between high and low quality productsand are not sufficiently educated to make informed decisions aboutproper product use.

ImmunoScore diagnoses and database could provide the answers to thesechallenges. Individuals and populations could be studied with respect tothe efficacy of a nutraceutical diet. ImmunoScore would either pave theway for more growth in curtain nutraceuticals, or perhaps point out thesale of “snake oil.” Individual products, or product lines could beendorsed as valid by ImmunoScore measurements.

The Wellness Industry is expected to grow. The Wellness Industryincludes the concept of “wellness insurance” to lower health care coststo individuals. This may provide yet another opportunity to leverageImmunoScore testing and data storage into the insurance industry.

In addition, workplace wellness as a concept has been used extensivelyin recent years by management in business and industry, healthprofessionals, fitness experts, and others. Well-designed andadministered programs deliver positive outcomes for employers as well asemployees. Because healthy employees cost less than employees sufferingfrom illness, ImmunoScore can be a part of employee insurance offered byemployers wanting the best and most affordable health care for theiremployees.

Analyses of the CIP database have shown the development of positive andnegative relationships for one variable with respect to another variable(for example, Rubella antibody and Hepatitis A antibody levels as isillustrated in FIG. 20D, and for example, Mumps antibody vis-a-vis HepA, Measles and Rubella, as shown in FIG. 20E). This type of analysiscould be extended to other variables regarding “wellness.” For example,fitness measurements could be incorporated (body mass index, cardiacfunction, etc.) into an overall immune fitness relationship.

Virtual Physicals™—Incorporate ImmunoScore Diagnostic and Database

The Virtual Physical™ is a comprehensive diagnostic screening procedurethat uses state-of-the-art technology to take a global look at apatient's body and identify a variety of conditions at early stageswhere intervention can be most helpful. A Virtual Physical™ may also beviewed as an integral component of a holistic, behavioral medicineprogram, where the body, and one's diet, exercise, and lifestyle habitsare viewed as a whole, determining where problems may exist and wherechanges might be required.

The Virtual Physical's™ early detection capability can uncoverasymptomatic and often life-threatening diseases generally notdetectable by physical exam or standard screening tests. This allows themanagement of disease in early stages, where medical therapy andtreatment options are typically less costly, less invasive and moreeffective.

Virtual Physical's™ comprehensive scan of an individual's body issignificantly more detailed than an X-ray. It covers: (a) the heart andarteries, identifying near microscopic amounts of plaque; (b) the lungsat the air cell level showing the earliest stages of smoke damage,emphysema, or lung cancer; (c) the spine, evaluating for osteoporosis,disc disease and other back problems; (d) internal organs for detectionof tumors, stones and cysts of all sizes; (e) aneurysms in the abdominaland chest cavities; (f) thyroid and parathyroid disease; (g) jointdisease; and (h) uterine, ovarian, and prostate disease.

In the interest of determining a patient's “totality of health,”ImmunoScore screening could accompany a Virtual Physical™ to add animmune health component to the virtual screening. It is possible thatinsurance will cover a Virtual Physical™ in the future, and ImmunoScoretesting and data storage could be incorporated into the patient'srecords that could be transferred to the patient's primary carephysician or specialist.

G. Women of Childbearing Age/Screening of Pregnant Women

A superpanel for women of childbearing age was described above inSection I.

In light thereof, ImmunoScore diagnostic tests and database storageavailability in the offices of obstetricians would greatly enableappropriate immunization of pregnant women as well as find correlates ofprenatal interest. In addition to screening pregnant women for theirimmune status regarding vaccine preventable diseases, ImmunoScorediagnoses and data management could also be of value in determining theimmune status of pregnant women regarding, for example, group Bstreptococcal infection, cytomegalovirus (CMV) infection, and otherinfectious diseases that may adversely affect the newborn, yet aretreatable prenatally. Early onset GBS infection has been the leadingcause of death attributable to infection in newborn infants for overthree decades, with over 6,000 cases a year in the United States(Vallejo, et al. 1994). Antibiotics have been used to good effect toprevent newborn GBS infection. There is also promising preliminary dataon an effective intervention to prevent CMV infection in newborns inpregnant women that has been published recently (Nigro, et al. 2005).All these treatments can be more advantageously administered usingImmunoScore technology.

FIG. 28. depicts an exemplary process flow for managing the immunestatus of women of child-bearing age. Beginning at 2801 the immunestatus of a women of child-bearing age is examined. At 2810 the vaccinepreventable diseases to which the woman is susceptible are identified aswell as the woman CMV infection status and pregnancy status. At 2820these three variables are used to generate healthcare recommendations,as follows. If the woman has not been infected with CMV and is notpregnant, she is advised to obtain immunizations for the identifiedvaccine preventable diseases. If she is an insured under a healthcareinsurance plan, or her healthcare is provided by an HMV or socializedmedicine entity she can be, for example, required to obtain theseimmunizations to save future treatment costs as well as to serve theutility of having a healthy population. If she has not been infectedwith CMV but is pregnant, she can be informed of extra precautionsregarding CMV status and pregnancy. Moreover, no immunization withattenuated vaccines is recommended or should be performed.

However, other immunizations should be recommended based upon currentCDC guidelines. If the woman is seropositive to CMV and is not pregnant,she can be advised or required, as the case may be, to obtainimmunizations for the identified vaccine preventable disease. Finally,if she seropositive for CMV and pregnant, no extra precautions should betaken regarding the CMV status unless there is an active primaryinfection. Moreover, no attenuated vaccine should be recommended oradministered. However, other immunizations can be recommended orrequired based upon current CDC guidelines. At 2830 a follow-upexamination of the women's immune status post-vaccination can beconducted, and, if she is not pregnant, the information can simply bestored in a system database. If she is pregnant, a post-natal follow-upcan be recommended or required, as the case may be, comprising MMRvaccination of the mother and monitoring of CMV status of the child.Finally, at 2840, based upon the post-vaccination follow-up at 2830 theefficacy of the administered vaccines can be evaluated as to whetherthey provide the necessary immunity to the vaccine preventable diseasesidentified at 2810.

The CIP clearly points out the need for antibody measurements in womenof child-bearing years. The obvious antibody to be examined is that forRubella, to which the women of SE Asia were shown to have levels belowaverage. Other important antibodies in women of child bearing years are,of course, those to group B Streptococcal organisms and others thataffect fetal development or those associated with neonatal illnesses.From an insurance and public health perspective, these are extremelyimportant issues.

H. Vaccine-o-Mat/Vaccine Distribution Network

In exemplary embodiments of the present invention, ImmunoScoretechnologies can be used to facilitate the easy dispensing of vaccinesto the public as well as giving the public access to their immunologicinformation. Therefore, in exemplary embodiments of the presentinvention a business analogous to the “Fotomat” photograph finishingstores, once located in malls and strip malls across America, can becreated. For purposes of the present description, this exemplaryembodiment of the present invention can be called “Vaccine-o-Mat.”Vaccine-o-Mats can be located in small buildings in corners of malls andstrip malls, as concessions in large chain stores such as Target orWal-Mart, or they can be located almost anywhere in appropriate marketsand one day be as ubiquitous as Starbucks Coffee centers. At aVaccine-o-Mat a member of the public can have his or her immune statuschecked and can receive any vaccines that he or she may be deficient in.If an individual steps on a rusty nail and doesn't remember the lasttime he or she had a tetanus booster he or she can simply drive to thenearest Vaccine-o-Mat, have a panel of assays containing tetanus and anyrelated compliments as conducted and determine then and there whether heor she needs a vaccine. What makes the Vaccine-o-Mat business possibleis instruments which can process large numbers of assays in a relativelyshort period of time, as noted above. One such instrument is the cobas e411 analyzer (Roche Diagonstics).

FIG. 29 depicts an exemplary process flow for use at a Vaccine-o-Mat. At2901, the customer's immune status is examined for vaccine preventablediseases and related immunologic information. It is further contemplatedthat a particular customer may want to have his or her bodily fluidsassayed for a wide variety of immunologic tests and not have themrestricted to vaccine preventable diseases. Therefore 2901 need not tobe strictly directed towards vaccine preventable diseases. At 2910,within 90 minutes the assay results can be processed to generaterecommendations for appropriate vaccines. This functionality dependsupon, as noted above, instruments which can process a large number ofassays in a relatively short amount of time. This concept allows forpartnering with large chain stores or malls where customers could maketheir first stop at the Vaccine-o-Mat to have their blood tested. Theycould then continue shopping and then return at the end of theirshopping excursion to receive any necessary vaccines and reportregarding their immune status. At 2920 appropriate vaccines can beadministered to the customer on site, and at 2930 the customer can beprovided with a printout of the assay results the updated vaccinationrecord and his or her database record from the ImmunoScore databasealong with instructions on how to access that information in the future.Finally, at 2940 all of the additionally required customer informationresulting from that particular visit is stored in the database forfuture reference.

One of the benefits of the ImmunoScore technology is the ability to linkdiagnostic testing of the immune system with rapid delivery ofmedication at the point of care (ideally, during the course of an officevisit). Thus, in exemplary embodiments of the present invention avaccine distribution network can be set up, for example, to link vaccinemanufacturers to physicians' offices—or other authorized vaccinedispensing personnel equipped with diagnostic facilities. Vaccinedistribution can also, for example, become part of the ImmunoScoredatabase tracking specific manufacturers' lots numbers to points ofsale. This can be important in getting timely information incorporatedinto the Vaccine Adverse Event Reporting System (VAERS).

FIG. 29A depicts exemplary envisioned interactions between variousparties according to an exemplary embodiment of the present inventiondirected towards vaccine distribution. Information gathered to anexemplary ImmunoScore database can, for example, be shared with thevarious agencies responsible for dicitating vaccination decisions.Unsuspected or unknown relationships regarding immune health or functioncan be, for example, “fished” or “mined” from a system database usingappropriate queries and analysis. In addition, in exemplary embodimentsof the present invention, suspected adverse events from vaccinationcould be addressed and acknowledged or dismissed, based upon informationgleaned from the system database.

With reference to FIG. 29A, various entities and institutions which can,for example, be involved in vaccine distribution or vaccine distributionnetwork are depicted. They include any vaccine manufacturers 29A05 whothrough vaccine sales provide vaccines to physicians or healthcareproviders 29A10. The physicians or healthcare providers 29A10 alsoreceive diagnostic testing kits and research services, such as, forexample, ImmunoScore vaccine diagnostic panels 29A01. The government29A15 has a variety of roles in a vaccine distribution network,including subsidizing or providing economic incentives to create orbuild a supply of vaccines by a transfer of funds to, or via taxincentives to, vaccine manufacturers 29A05. The government can furthersubsidize or fund HMOs 29A25 and in this context the Veteran'sAdministration, described above can be considered one of them.Additionally, the government 29A15 can mandate vaccine benefits tocertain segments of the population and those can be provided by HMO29A25 or equivalents. Finally, the government 29A15 can itself accesspersonalized immune status data as to individuals or populations orsub-populations 29A12 for a variety of research or health managementpurposes. The CDC and ACIP 29A50 can receive input fromPhysicians/Healthcare Providers 29A10 as well as from a vaccine statusdatabase 29A30. Vaccine status database 29A30 can be generated from anImmunization Registry 29A40 set up by the CDC, ACIP or other similarinstitutions or bodies to maintain immunization records for thepopulation so as to better know who should be vaccinated. FIGS. 29B and29C, described below illustrate improving connectivity between entitiesand organizations who could access and utilize ImmunoScore informationin this context, allowing the benefits of ImmunoScore to be ubiquitouslyavailable.

I. Consumer Accessibility to Immunologic Information

Americans are playing a risky game of sexual roulette, according to anew poll finding that only 39 percent of respondents always ask a newlover if they are infected with HIV. The poll, taken by Zogby forMSNBC.com also found that 73 percent of respondents were involved in amonogamous relationship, and that 66 percent of those surveyed had hadunprotected sex while under the influence of alcohol. While 39 percentof respondents said they always asked whether a new partner is infectedwith HIV or other sexually transmitted diseases, 31 percent said theynever discuss the touchy issue with a new partner. Moreover, the surveyfound that 15 percent of Americans had paid for sex, 35 percent ofrespondents said they had been with between one and five sexualpartners, and 19 percent said they had had more than 25 partners.

In exemplary embodiments of the present invention this “risky business”can be ameliorated. Accordingly, at the Vaccine-o-Mat described above,individuals can have their immune status tested by conducting, forexample, an STD assay panel, as described in Section I above, which canthen be shown to potential sexual partners to fully disclose theimmunologic risks that may be involved in any proposed liaison. Forexample, a couple can stop at a Vaccine-o-Mat near a romantic restaurantof their choice. They can have the assays conducted and go off to dine.If things are going well, by the time their coffee has arrived they canobtain each other's immune status and be off—either alone ortogether—depending upon the ImmunoScore results.

Alternatively, for example, someone worried by past promiscuities canroutinely procure his or her immune status at the local Vaccine-o-Mat in90 minutes, and put any worries to rest, or at least know what they arefacing.

J. Immunoscore Connectivity Via Interapplication Translator/DataIntegrator

In many exemplary embodiments according to the present invention, thepower of an ImmunoScore diagnosis and database lies in the interactionof the database with many different organizations, as shown in FIG. 29B.Use of a web services interconnector to provide this connectivity isillustrated in FIG. 29C, next described. The CDC, the government (orgovernments, for that matter), health maintenance organizations, vaccinemanufacturers, and physicians would all be able to interact with thedatabase and each other to make the best possible decisions regardingthe health and welfare of the citizenry.

With reference to FIGS. 29B and 29C, a number of entities andorganizations who could access and utilize ImmunoScore information areshown. FIG. 29B shows a complicated information exchange structurewherein each entity involved has to set up a separate communicationsline or pathway to each of the other entities in the network. This caneasily be remedied, as shown in FIG. 29C, by utilization of anInterapplication Connectivity Provider 29C50 which can interconnect thevarious individual and sometimes proprietary computer systems, computernetworks, databases, and applications of each of the individual entitiesparticipating in the vaccine distribution/creation network so that theycan talk to each other. This technology is often referred to asinterapplication connectivity or interapplication translation. Oneexample of such a interapplication connectivity provider is the IBM, inparticular the IBM Web Services Centers Of Excellence. Additionally,Enterprise Computing service companies, such as, for example, EDS alsoprovide products which link different and disparate computing platformsso that they can exchange data and information in an efficient manner.

The CIP database has only scratched the surface of what can be capturedand shared by a large ImmunoScore database, but important informationcan be gleaned from this database, such as it is, of use to governmentsources, patients, physicians, and insurers. Demographic informationregarding crowding and sanitary facilities has been shown to correlateto degrees of protection to vaccine-preventable diseases in thepopulations examined. If the database were to also include informationregarding the movement of patients (for instance), much usefulinformation could be shared among these concerned groups.

K. Immunologic Informatics Based Life Insurance Underwriting

In the exemplary embodiments of the present invention ImmunoScore datacan be used to optimize the underwriting of life insurance.Additionally, assuming that regulatory restrictions are not preclusive,ImmunoScore data can be used by companies which provide both life andhealth insurance to the same clientele. The use of ImmunoScoretechnology for these purposes is depicted in the exemplary process flowchart of FIG. 30.

With reference to FIG. 30, at 3001 an individual's immune status can beexamined and any diseases to which he or she is susceptible identified.At 3015, by accessing Business Rules Database 3010, the probability ofdeath of the individual given the immune status identified at 3001 canbe computed. At 3016 the cost of insuring that individual, based on theprobability of death of years to death calculated in at 3015 can becomputed and premiums can be set at 3020. It is noted that the term“death” appearing in FIG. 30 is shorthand for “years remaining untildeath.”

Additionally, at 3002 all combinations of possible prophylactictherapies can be generated given the immune status obtained at 3001.From these combinations, at 3005, the probability of time (generally inyears) to death given the immune status and the various combinations ofprophylactic therapies can be computed. Such computation, at 3005,exchanges data with Business Rule Database 3010. For convenience, twoBusiness Rules Databases 3010 are depicted in FIG. 30; in exemplaryembodiments of the present invention there could be one or many BusinessRules Databases each devoted to a specific informational domain. In thedepicted exemplary embodiment of FIG. 30 they could most likely becombined inasmuch as they are providing information which allows asystem to compute the probable time to death given an immune status.However, the Business Rules Database on the right side of the figure mayrequire more complex information in order to also factor in theavailable set of possible preventive therapies for each identifieddisease.

At 3016, the outputs of 3015 and 3005 are input to allow the exemplarysystem to compute the cost of insuring the given individual. At 3021 thesystem can select the two or three best sets of prophylactic therapiesfrom the information generated at 3002, and at 3025 it can offer theseprophylactic therapies to the client with a proviso that the lifeinsurance premium set at 3020 in absence of factoring in prophylactictherapies could be lower by (x) if the client chooses to undertake theprophylactic therapies. Alternatively, at 3030 it may be in an insurancecompany's interest to pay for the prophylactic therapies, i.e., offeringthem to the insured for free, if the cost of the prophylactic therapiesis less than the present value of the expected savings to the lifeinsurance companies by the insured having the prophylactic therapiesperformed. This can be expressed, for example, as:

PT cost<PV{(death benefit)*[(Prob(death|no IS,no PT)−Prob(death|IS,PT)]}

Thus, if at 3030 such an offer is made, any premium adjustment at 3020can be diminished or completely reduced. The function of 3030 is toincrease the profits to the life insurance company by not onlyidentifying the premium which it would charge the insured but also,based on the immune status data obtained during the underwriting process(or during an annual audit process), to identify prophylactic treatmentsthat could be offered to increase the time to death for the sameindividual thus allowing the insurance company to continue to earn thereturn on the cumulative premiums prior to having to pay the deathbenefit to the survivors.

It is also noted that at 3021 where the 2-3 best sets of prophylactictherapies are found the term best is really a function of how much theprobable time to death is increased. Finally, the availability ofprobable time to death given a certain immune status and certainprophylactic therapy can be computed using the following equation asnoted in FIG. 30:

Prob(death|IS and PT)=P(CD|PT and IS)*P(D|CD and IS)+P(not CD|PT andIS)*P(D|not CD and PT IS)

When offering prophylactic therapies to an insured, unique opportunitiesarise for insurance companies providing both life and health benefits. Ahealthier insured lives longer and uses less health care, resulting intwofold savings for an insurer. Because such a life insurance companyalso approves health care expenditures, there is no red tape or customereffort spent on securing approval for any offered or recommendedprophylactic therapies. Thus, in such contexts, the real worldoptimizations can actually converge on the theoretical optimizationscalculated by an ImmunoScore analysis as depicted in FIG. 30. This can,in exemplary embodiments, increase QOL for insureds and profits for theinsurers, as well, hopefully.

Patient commonalities, as revealed by analyses of the CIP database,could be visualized. For example, if a population immigrating fromEastern Europe were shown, in general, to have lower protection againsta specific disease or diseases, that information could, for example, beof interest to health/life insurance companies.

Described below is a second exemplary embodiment of the presentinvention wherein ImmunoScore data can be used to optimize theunderwriting of life insurance. The second embodiment can be used incombination with or separate from the method described above.

The underlying concept of this method involves ascertaining anindividual's likelihood of surviving an unknown, unanticipated, orotherwise unaccounted for disease. For example, the disease may be onethat is not considered at step 3001. For example, new diseases emerge(e.g., HIV in the 1980's) or old diseases become resistant to therapies(e.g., antibiotic resistant forms). Thus, ImmunoScore is used to assessthe ability of an individual's immune system to react favorably to oneof these challenges.

For example, the magnitude and direction of the combined response ofTh1, Th2, Th17, and Treg can be used to assess an individual's abilityto resist new diseases. In some embodiments, the probability of deathgiven an immune status (Prob(death|IS)) is inversely related to themagnitude of the combined response. The larger the magnitude of thecombined response (as trended over time or at one time point), the moreout-of-balance the immune system is, increasing the chances theindividual will contract diseases, thereby increasing the appropriatelife insurance premium.

This use of ImmunoScore technology may be easier to implement than theother life insurance model, because less information is requiredregarding diseases, prophylactic therapies, and how they affect theindividual's remaining lifetime. Nevertheless, this second method canstill improve the stratification of individuals to set and/or adjustpremium levels. The two methods can be used in combination. The firstmethod can be used on all known diseases or on a subset (perhaps only1-10 diseases) to improve the results obtained from only using thesecond method.

L. Diagnosing and Managing Immunosenescence in the Elderly

Human aging is associated with progressive decline in immune functionsand increased frequency of infections. Morbidity and mortality due toinfectious disease is greater in the elderly than in the young, at leastpartly because of age-associated decreased immune competence, whichrenders individuals more susceptible to pathogens (Pawelec, et al.2005). A decline in immune function is a hallmark of aging that affectsthe ability to resist influenza and respond to vaccination. Anaccumulation of dysfunctional T cells may be detrimental underconditions of chronic antigenic stress (chronic infection, cancer,autoimmunity). The most important changes occur in T-cell immunity, andare manifested particularly as altered clonal expansion of cells oflimited antigen specificity (Fulop, et al. 2005). This is most marked inthe CD8⁺ T cell subset, which displays a decrease in both responsivenessand normal function. Normally, CD8⁺ T cells appear to be stronglyassociated with cytolytic activity, either by direct killing ofantigen-bearing target cells by granule-mediated exocytosis orFas-mediated cytotoxic mechanisms. In addition, it is suggested thatantigen-activated CD8⁺ T lymphocytes can eliminate or control viralinfection by secretion of antiviral cytokines, such as gamma interferon(IFN-γ) and tumor necrosis factor alpha (TNF-α). IFN-γ production byCD8⁺ T cells can have both local and systemic consequences, whereascytotoxins such as perforin are cytolytic for the cells that come indirect contact with the cytolytic T lymphocytes (CTL).

The output of the T cell pool is governed by output from the thymus andnot by replication (Aspinall and Andrew, 2000). As thymic T cellproduction diminishes with age, a decline in contribution made by thymicemigrants to the naive T cell pool occurs (Mackall, et al. 1995).Diminution in the size of the naive T cell pool is a common finding withaging, and is a consequence of reduced thymic output (Kurashima, et al.1995). Thymic atrophy is thought to result from a failure of the thymicmicroenvironment to support thymopoiesis in old age and recent evidencesuggests that a decline in interleukin-7 (IL-7) expression may limitthymocyte development by restricting combinations of survival,proliferation and rearrangement of the beta chain of the T cell receptor(Andrew and Aspinall, 2002). Therapeutic intervention with IL-7 andderivatives has been shown to reverse thymic atrophy in old animals andalso lead to improved immune function compared with age and sex matchedcontrol animals (Aspinall, 2005).

The CD8⁺ T cell repertoire becomes less diverse in old age due toreduced thymic output and the accumulation of clonally expanded memoryCD8⁺ T cells as a consequence of prolonged antigenic stimulation.Clonally expanded T cells are usually CD8⁺ and show an increasedincidence with age, so far it seems that clonal expansion is not due tomalignancy but may follow antigen stimulation. It has been suggestedthat repeated or persistent infections with viruses such as influenza,cytomegalovirus (CMV), and Epstein-Barr virus (EBV) may drive responsesthat result in large T cell clones. Longitudinal studies suggest that aset of immune parameters including high percentages of peripheralCD8⁺CD28⁻CD57⁺ T cells, low CD4⁺ and B cell counts, and poor T cellproliferative responses to mitogens is associated with decreasedremaining longevity in the free-living very elderly (>85 years). CMVseropositivity is closely associated with increases in the size of theCD57⁺CD8⁺ T cell pool, which is thought to represent a highlydifferentiated population of late memory cells. Furthermore, CMVseropositivity is associated with increases in CD8⁺ count in old age andhas been documented to have negative influences on immune parameters inthe very elderly. A group concluded that the “obsession” of a largefraction of the entire CD8⁺ T cell subset with one single viral epitopemay contribute to the increased incidence of infectious disease in theelderly by shrinking the T cell repertoire for responses to otherantigens. Like CMV, EBV manages to persist for the lifetime of theinfected host. During chronic asymptomatic infection in healthyindividuals, EBV resides in memory T cells. Expansion of peripheral CD8+CD28− T cells in response to chronic EBV infection has been linked torheumatoid arthritis. The clinical consequences of these changes are asyet not well defined, except for their extremely important negativeimpact on defense against infections. Considering the public healthconsequences of decreased immune competence in old age, strategies forimmune response modulation are desirable to decrease the health burdenfor the elderly and improve their quality of life.

Features of successful aging have been associated with well-preservedimmune function while poor survival is predicted by high CTL counts, lownumbers of B cells and poor responses by T cells to polyclonalstimulation. The phenomenon of replicative sensescence has beenassociated with these changes and relates to a finite number ofdoublings (25-30 cycles) after which cell cycle arrest occurs. In CTLs,this growth arrest is associated with increased production of severalpro-inflammatory cytokines, resistance to apoptosis and loss of theco-stimulatory molecule, CD28, required for optimal stimulation of CTLs.In older adults, greater than 50% of CTLs fail to express CD28 and thesecells are resistant to apoptosis.

The loss of CD28 expression due to replicative senescence has beenassociated with a number of the adverse effects of aging on immunefunction. Although the frequency of influenza virus-specific CTLs doesnot appreciably change with age, the decline in CTL activity againstinfluenza may be due to a loss of antigen-specific proliferation and/ordiminished lytic activity. Normal loss of CD28 expression during CTLactivation and the potential for these cells to undergoactivation-induced cell death, may be confused with the loss of CD28with replicative senescence and resistance of CTLs to apoptosis.Furthermore, the role of cytokines (such as, for example, IL-2, IL-7,and IL-15) in preventing activation-induced cell death and age-relatedchanges in the production of these cytokines create a complex array ofinteractions that may confound the interpretation of in vitroexperiments. Understanding the complexity will provide an opportunity tooptimize the CTL response to vaccination by manipulating CTLs thatretain their replicative capacity in response to appropriate antigenicstimuli.

Currently, influenza vaccination of elderly individuals is recommendedworldwide. A recent study looked retrospectively at influenza vaccineefficacy in individuals aged 65 years or older. They found that in homesfor elderly individuals, that vaccines were not significantly effectiveagainst influenza, influenza-like illness, or pneumonia. Moreencouragingly, vaccine performance was improved for admissions to thehospital for influenza or pneumonia, respiratory diseases, and cardiacdisease. This group concluded that the usefulness of influenza andpneumococcal vaccines was modest. On the same day the Jefferson reportwas published online, the American Medical Directors Associationreleased a special announcement regarding the Jefferson study andinfluenza vaccine recommendations for the elderly(http://www.amda.com/newsroom/092205 vaccines.htm). While notdisagreeing with the tenets of the study, they continued to recommendfor vaccination of the elderly because influenza vaccination iseffective at preventing severe illness, secondary complications, anddeaths. They also reiterated that the CDC recommends influenzavaccination for people age 65 years and over and for all persons inlong-term care facilities (http://www.amda.com/newsroom/092205vaccines.htm). Both groups concluded that better influenza vaccines thatoffer more protection in older persons are desirable and a high priorityof influenza researchers.

The threat of pandemic influenza has increased with the directtransmission of highly pathogenic avian H5N1 viruses to humans.Continued reliance in killed virus or subunit vaccines will leave adultsat significantly higher risk of illness, disability and death in theevent of an influenza pandemic. Research that increases ourunderstanding of how immunosenescence affects the cell-mediated responseto influenza and vaccine responsiveness is critical to the developmentof effective pandemic influenza vaccines for older people. In theabsence of influenza vaccines that target these defects, an influenzapandemic will have a significant impact on older people and quicklyoverwhelm the health care system.

On Aug. 8, 2005 the CDC has stated that the effectiveness of inactivatedinfluenza vaccine depends primarily on the age and the immunocompetenceof the vaccine recipient and the degree of similarity between theviruses in the vaccine and those in circulation. When the vaccine andcirculating viruses are antigenically similar, influenza vaccineprevents influenza illness among approximately 70-90% of healthy adultsaged <65 years. Children aged ≧6 months can develop protective levels ofanti-influenza antibody against specific influenza virus strains aftervaccination, although the antibody response among children at high riskfor influenza-related complications might be lower than among healthychildren. In addition, no efficacy was demonstrated among children whohad received only one dose of influenza vaccine, illustrating theimportance of administering two doses of vaccine to previouslyunvaccinated children aged <9 years. Older persons and persons withcertain chronic diseases might develop lower post-vaccination antibodytiters than healthy young adults and thus remain susceptible toinfluenza infection and influenza-related upper respiratory tractillness (http://www.cdc.gov/flu/professionals/vaccination/efficacy.htm).While current vaccines are cost-saving, new influenza vaccines willlikely be needed to avoid the crisis anticipated in health care relatedto the general aging of the population.

Another component to the aging immune system is the relationship betweeninnate immunity and inflammation. During evolution the human was set tolive 40 or 50 years; today, however, the immune system must remainactive for a much longer time. This very long activity leads to achronic inflammation that slowly but inexorably damages one or severalorgans. This is a typical phenomenon linked to aging and it isconsidered the major risk factor for age-related chronic diseases.Alzheimer's disease, atherosclerosis, diabetes, sarcopenia, and cancerto name several, all have an important inflammatory component, thoughdisease progression seems also dependent on the genetic background ofindividuals. Inflammatory genotypes are an important and necessary partof the normal host response to pathogens in early life, but theoverproduction of inflammatory molecules might also cause immune-relatedinflammatory diseases and eventual death later.

Most age-related diseases have complex etiology and pathogenicmechanisms. The clinical diagnosis and therapy of these diseasesrequires a multidisciplinary approach with progressively increasedcosts. A body of experimental and clinical evidence suggest that theimmune system is implicated, with a variable degree of importance, inalmost all age-related or associated diseases. Both the innate and theclonotypic immune systems are usually involved in the pathogenesis ofthese chronic diseases (Caruso, et al. 2004; Pawelec, et al. 2002).Several functional markers of the immune system may be used either asmarkers of successful aging or conversely as markers of unsuccessfulaging. A combination of high CD8⁺ and low CD4⁺ and poor T cellproliferation has been associated with higher mortality in very oldsubjects. Old men carrying an anti-inflammatory IL-10 high-producergenotype or a pro-inflammatory IL-2 low-producer genotype show thelowest values of CD8+ cells. This study, however, did not do afunctional assessment of T cells.

In a mouse model looking at T cell subset patterns, researchers foundthat a composite combination of subset values was a significantpredictor of longevity among genetically heterogeneous mice, with astrength of association higher in older mice than among the young.Developing useful biomarkers of aging has proven to be remarkabledifficult, in part because many age-sensitive variables tested ascandidate biomarkers are sensitive to genetic and nongenetic influencesother than aging. Any individual assay, for example a test of a specificT cell subset in a single blood sample, is likely to have a good deal ofuncertainty, but the combination of results from related tests mayincrease the signal-to-noise ratio and thus provide stronger predictivepower than any single assay by itself. In humans, ImmunoScore testingwould help build the models of T cell subset patterns. Possible coursesof therapy would then be ideally tailored to meet the needs of theindividual and not a “best guess, one size fits all” course oftreatment.

Clearly, the population aged ≧65 years would be better served byImmunoScore diagnostics rather than the current state of affairs. Ablanket recommendation for an influenza or pneumococcal vaccination forthe entire elderly population may not be in the best interest of anindividual being immunized. ImmunoScore diagnostic tests could, forexample, first reveal levels of protective antibody tovaccine-preventable diseases. Of particular interest would be antibodylevels against influenza, pneumococcal infection, tetanus, diphtheria,pertussis, hepatitis, varicella, CMV, and EBV. Just as important asdetermination of antibody levels in elderly patient sera, ImmunoScorediagnostic tests could reveal the status of cellular components of theimmune system. The proportion of naive/committed T and B cells would becrucial for further recommendations by the attending medical staff. Astherapeutic interventions are developed for dealing withimmunosenescence, the ImmunoScore diagnostic information regardingindividuals and compiled database information will shed valuable lightonto the effects of treatments on the immune system. As the populationages, strategies for immune response modulation are desirable todecrease the health burden for the elderly and improve their quality oflife.

A preliminary immune risk phenotype (IRP) has been developed fromlongitudinal studies of the elderly. Immune system measurementsconsisted of determinations of T-cell subsets, plasma IL-6, IL-2responsiveness to conconavalin A, and CMV and EBV serology. Regressionanalyses indicated that the IRP and cognitive impairment togetherpredicted 58% of observed deaths. This type of analysis would be avaluable adjunct to assessing insurance premiums.

The following table captures exemplary desirable analytes to monitor inthe population as individuals age. A database storing the results ofsuch assays could ensure that a given individual's analyte levels couldbe tracked over time rather than merely captured as a snapshot.

TABLE 1 Alterations in the T-cell compartment with age AlterationAnalyte ↑ CD45RO⁺ cells ↑ CD95⁺ cells ↓ CD28 expression ↑ CD152expression ↑ killer cell lectin-like receptor G1 ↓ apoptosis of CD8cells ↑ apoptosis of CD4 cells ↓ IFN-γ production ↓ IL-2 production ↓telomere lengths ↓ telomerase induction ↑ DNA damage ↓ DNA repair ↓stress resistance and heat-shock protein expression

Thus, in exemplary embodiments of the present invention anImmunosenescence superpanel can be defined, comprising the followingpanels:

Meningococcal Diagnostic Panel; Persistent Immunity Induced by ChildhoodVaccines; and Immunosenescence Diagnostic Panel

The first two panels are defined in Sections IA1 and IA3 of theImmunologic Information Patent, and an Immunosenesence panel can, forexample, be defined as follows.

Human aging is associated with progressive decline in immune functionsand increased frequency of infections. A decline in immune function is ahallmark of aging that affects the ability to resist influenza andrespond to vaccination. The most important changes occur in T cellimmunity. An accumulation of dysfunctional T cells may be detrimentalunder conditions of chronic antigenic stress (chronic infection, cancer,autoimmunity).

Exemplary Alterations in T-cell compartment to monitor:

Typical Alteration Analyte Increased CD45RO⁺ cells Increased CD95⁺ cellsDecreased CD 28 expression Increased CD152 expression Increased Killercell lectin-like receptor G1 Decreased Apoptosis of CD8⁺ cells IncreasedApoptosis of CD4⁺ cells Decreased IFN-γ production Decreased IL-2production Decreased Telomere lengths Decreased Telomerase inductionIncreased DNA damage Decreased DNA repair Decreased Stress resistanceand heat-shock protein expression

Other analytes of particular interest in an immunosenescence assay panelcan, for example, include:

-   -   Antibody to CMV    -   Antibody to EBV    -   Antibody to influenza    -   Antibody to pneumococcal disease    -   Antibody to pertussis    -   Antibody to tetanus    -   Antibody to diphtheria    -   Plasma levels of IL-6    -   Th1/Th2 components as described below:

Th1 Th2 Cytokines Receptors Cytokines Receptors INF-γ CCR5 IL-4 CCR3TNF-α CXCR3 IL-5 CCR4 IL-2 CCR1 IL-6 CCR8 IL-12 IL-10 CRTh2 IL-13

FIG. 31 depicts an exemplary process flow for managing immunosenescentindividuals, either in a health care provider or a health care insurercontext.

In exemplary embodiments of the present invention immunosenescense in anindividual can be managed using the process exemplary flow depicted inFIG. 31. With reference thereto, at 3101, an elderly individual's immunestatus can be examined. This can be accomplished by conducting one ormore assay panels as described above in Section I. At 3110, the vaccinepreventable diseases that the elderly individual is susceptible to canbe identified at the same time the individual's CMV infection statustogether with other relevant markers of an immune system competence canalso be determined. At 3120 vaccine and/or other healthcarerecommendations can be made based upon the immune status examined at3101. Additionally, a separate T cell compartment can be assessed. At3130, the individual can be immunized for vaccine preventable diseasebased upon his or her immune system's ability to response tovaccination. Using the ImmunoScore data, the individual can beclassified as either (1) immunocompetent (2) immuno-deficient or (3)somewhere inbetween immunocompetent or immuno-deficient. At 3130 animmuno-competent individual can be vaccinated as recommended by currentACIP recommendations. An immuno-deficient individual would need to bemanaged using different measures than routine vaccination. Such measurescould include, for example, adoptive transfer of a compartment of T or Bcells or extraordinary hygiene measures. The individuals who fallsomewhere between immunocompetence and immuno-deficiency need some kindof hybrid health management between standard vaccination andimmunoadjuvant therapies such as adoptive transfer of T or B cells andextraordinary hygiene measures. At 3140, the elderly individual's immunestatus can be followed up post vaccination or post treatment and theseresults stored in the system database. At 3150, this information can beused to evaluate the efficacy of the vaccination or other therapies asto their abilities to provide the necessary immunity to the identifieddiseases.

M. Frozen Storage of Naive Immune Cells (IRP Considerations)

As previously described, the immune risk phenotype (IRP) is an emergingconcept—predicting mortality based on CMV seropositivity (Pawelec, etal. 2005). Pawelec, et al. maintained that the manner in which CMV andthe host immune system interact is critical in determining the IRP andis hence predictive of mortality. The consequences of IRP is earlyexpression of immunosenescence. Immunosenescence leads to: a) decreasedT- and B-cell responses to foreign antigen; b) increased responses toself antigens; c) increased morbidity and mortality to infectiousdisease; and d) decreased response to vaccine antigens.

Greater elucidation of the IRP and its consequences is to be expected inthe future. Genetic screening at a very early age could be predictive ofimmune health at a much more advanced age. The ImmunoScore diagnosticscreen could be performed from a heel stick done at birth, and a child'sbaseline immune status could almost instantaneously be generated.Pre-natal screening tests could also be developed in the future as animmunodiagnostic tool.

Concerned parents may wish to store their child's cord blood as a sourceof hematopoietic progenitor cells that could be stored (at a cost to theparents or the insurers) for that child for treatment of developing IRPsymptoms much later in life. Umbilical cord blood (UCB) is currentlyused as a source of these hematopoietic progenitor cells as analternative to the bone marrow or peripheral blood for treatment ofseveral onco-hematological diseases (Adami, et al. 2005).

On Apr. 18, 2005 the Institute of Medicine (IOM) issued a reportrecommending that a new cord blood coordinating center—similar to theexisting National Marrow Donor Program—be set up to ensure astandardized and interconnected national system to cost-effectivelystore and distribute these cells.

ImmunoScore diagnostics shows the need for storing cord blood.

Another application for ImmunoScore diagnostics is to link storage andanalysis of naive cells of the immune system (innate or adaptive), asnext described.

T cells currently used for adaptive immunotherapy trials are selectedfor their capacity to produce high levels of IFN-γ and for their abilityto efficiently and specifically lyse relevant target cells (Dudley andRosenburg, 2003; Yee, et al. 2002). However, it was found that CD8⁺ Tcells that acquire complete effector properties and exhibit increasedanti-tumor activity in vitro are less effective at triggering tumorregressions and cures in vivo (Gattinoni, et al. 2005). While theprogressive acquisition of terminal effector properties is characterizedby pronounced in vitro tumor killing, in vivo T cell activation,proliferation, and survival are progressively impaired. These findingssuggest that the current methodology for selecting T cells for transferis inadequate (Gattinoni, et al. 2005). It is clear that new solutionsare needed to generate more effective anti-tumor T cells for thedevelopment of experimental human adoptive transfer-based therapies.

The indication is that storage of naive T and B cells is important forindividuals who will become immunocompromised later in life, whetherthose cells come from that individual or from another source. Naivecells would also not necessarily be isolated from cord blood, but couldalso be isolated from bone marrow or peripheral blood. In addition,screening methods can be used to characterize those immune cellsregarding cell surface characteristics and cytokine expression. Heretoo, ImmunoScore can be used to a distinct advantage.

N. Vaccine Use Outcome/Design

Currently, what the public considers vaccines are designed as aprophylactic means to avoid illness caused by infectious disease. Inpractice, agents used to promote an immune response as a therapeuticcourse of action for cancer or immunotherapy have also been termed“vaccines.” It is the intent of the ImmunoScore design to be able tomonitor changes in an individual's immune system in relation to aprophylactic or therapeutic vaccine and enable the individual patientand his physician to make the best possible decisions regarding thepatient's immune system health regarding prophylactic vaccination,therapeutic vaccination, or other therapeutic treatment in attempt to“shift” the immune system of that patient. In addition, the ImmunoScoredatabase will compile important population data regarding demographicsand population genetics.

O. Research Services

In exemplary embodiments of the present invention ImmunoScoretechnologies can be used to provide research services, such as, forexample, for clinical trials in the following areas:

1. vaccines;2. transplants;3. adaptive immunotherapy;4. population modeling; and5. government applications.

P. Immigration Consulting

Testing the immigrant population for vaccine-preventable diseases isanother embodiment of the invention. Governments are very interested inthe immunization status of individuals and families immigrating intotheir countries. The invention can rapidly provide the results of assaysto governmental authorities for all required immunizations. There wouldbe no need to rely on paperwork—a diagnostic examination would yieldmore suitable data regarding immune status. The current vaccinationrequirements for immigration into the United States are for measles,mumps, rubella, polio, tetanus, diphtheria, pertussis, influenza,hepatitis B and any other vaccinations recommended by the AdvisoryCommittee for Immunization Practices (ACIP). Current recommendations ofthe ACIP also include varicella, Haemophilus influenzae type B, andpneumococcal vaccines. The current law requires all individuals applyingfor status as a lawful permanent resident (either by applying for animmigrant visa abroad or for adjustment of status in the United States)to establish that they have been vaccinated. Nonimmigrant (temporary)visa applicants are not required to comply with the vaccinationrequirements as a condition of visa issuance, but must comply if theyapply for adjustment of status at a later date (Immigration andNaturalization Services, 2001).

One or more exemplary ImmunoScore diagnostic panels could, for example,be provided to INS or other immigration authorities as a means todetermine the immune status of immigrants. In practice, ImmunoScorediagnostic testing can be more cost-effective than a paper record trailand more likely to be reliable as an accurate assessment of immunestatus of individuals relocating to the United States.

Additionally, the Institute of Medicine (IOM) has concluded that theUnited States quarantine system is in need of a strategic overhaul. TheIOM reports that the United States once had 55 federal quarantinestations, but the perception that microbial threats had been controlledled to dismantling of most of the system in the 1970s. However, nearly40 new infectious diseases have been identified since 1973, andbioterrorism has become a serious concern. The 25 stations that willmake up the expanded quarantine station system now receive more than 75million international travelers a year, according to IOM reports. Thestations screen travelers, refugees, immigrants, animals, and cargo fordisease agents shortly before and during their arrival. However, thequarantine system relies on a much broader network that includes localpublic health agencies, hospitals, customs agents, agriculturalinspectors, and others, the IOM said.

The IOM recommended the following:

The quarantine stations, the CDC, and the DGMQ (called the quarantinecore) should lead the effort by developing a national strategic planwith uniform principles and outcomes. The quarantine core should shiftits main focus from inspecting people and cargo at ports to leading theactivities of the overall quarantine system. The strategic plan shouldhelp participating government agencies and other groups in the system toprioritize activities and focus resources on the greatest risks.

The quarantine core should work with partners in the quarantine networkto define or redefine each group's roles, authority, and communicationchannels.

The quarantine system needs enhanced skills, more people, more training,more space, and better use of technology to fulfill its evolving role.An example of technology cited in the news release was targeted use ofpassenger locator cards that could be used on flights to and fromcountries with disease outbreaks. The cards would log passenger seatnumbers and contact information in a scannable format. This couldsimplify tracking of passengers potentially exposed to disease, such asthose who flew to the United States from Sierra Leone in 2004 with a manwho later died of Lassa fever.

The quarantine core must review its methodology periodically to ensurestations are in the best places and appropriately staffed.

The quarantine core must have plans, capacity, resources, and “clear andsufficient legal authority” to respond quickly to surges in activity atone or more ports.

The core must define and fund a research agenda to measure theeffectiveness of its procedures. The committee found that many routinesat quarantine stations are based on experience and tradition and lack ascientific basis.

The core must use scientifically sound methods to measure theeffectiveness and quality of its operations, including assessingperformance of critical functions throughout the system. It must alsoaddress any shortfalls that come to light.(http://www.nap.edu/books/030909951X/html).

ImmunoScore technology could be useful at such immigration port of entryscreening points. There is a need for global health that can not beunderstated. The cost of failure could be extremely high. There arepeople moving around the globe and among the states with clear healthneeds, and they are currently moving without the ability of governmentauthorities to track them.

Additionally, ImmunoScore technologies can be used to discover linksbetween immunological phenomena. For example, from the results ofGreenway (discussed above in Section I regarding the immigrant panel) apossible link between TB infection and HepB prevalence can beinvestigated by analyzing sera from an immigrant population for bothactive TB and HepB seropositivity. It is possible that more than oneco-infection may be found in this manner. For example, in the followingstudy, A high prevalence of hepatitis B virus infection amongtuberculosis patients with and without HIV in Rio de Janeiro, Brazil,Blal C A et al Eur J Clin Microbiol Infect Dis. 2005 January;24(1):41-3, such a correlation was in fact found.

The Blal study sought to investigate the prevalence and exposure factorsassociated with hepatitis B infection in tuberculosis patients with andwithout HIV type 1 co-infection. The presence of hepatitis B virusserological markers was investigated in a retrospective study. Theseroprevalence of hepatitis B virus in patients with tuberculosis onlywas 14.6%, and in tuberculosis patients co-infected with HIV itincreased to 35.8%. In patients with HIV and tuberculosis co-infection,homosexuality constituted the principal exposure factor, while intuberculosis patients without HIV, a gradual increase in hepatitis Bvirus seroprevalence was noted along with increasing age. These resultsdemonstrate that hepatitis B infection is highly prevalent intuberculosis patients in Brazil and suggest that a vaccination programfor the general population should be considered in order to preventfurther hepatitis B infections.

Q. Disaster Survivors: Immunizations, Recovery, Prognosis and Treatment

In exemplary embodiments of the present invention, rapid responseservices to disaster survivors can be provided. FIG. 32 depicts anexemplary process flow for such an application.

At 3201 a disaster survivors' immune status can be examined using one ormore ImmunoScore assay panels as described above in Section I. At 3210the vaccine preventable diseases to which the survivor is susceptiblecan be identified and simultaneously the cellular component of his orher immune system can be assessed to get an immediate post disasterbaseline. At 3220 vaccination and healthcare recommendations can begenerated based upon antibody levels to the identified to the assayvaccine preventable diseases. At 3230 immunization can be carried outand at 3240 follow-up examination of the survivor's immune status can beadministered and the results stored in the system database. Furtherscreening of T cell components of the immune system is recommended forall survivors regardless of their psychological state at the time inorder to develop data regarding post-traumatic stress disorder. Finally,at 3250 the efficacy of the vaccine and/or therapies can be evaluated asto their ability to provide necessary immunity to the identifieddiseases.

There are many different possible responses of an individual to an eventperceived as potentially life-threatening. It is difficult to predictlong-term responses to trauma based on the acute response to a traumaticevent. If physiological risk factors are important in understanding howpsychopathology develops, then ImmunoScore measurements can provideinvaluable research information and possibly identify treatments yet tobe defined. This could pave the way to personalized medicine. FIG. 33illustrates possible responses to trauma.

With reference thereto, at 3301 a Disaster Trauma occurs. There are twopathways leading from 3301, namely, Normal Response Factors 3305 andPathological Response Factors 3303. A Normal Response Factors 3305pathway from Disaster Trauma 3301 leads to Recovery at 3310. However,Pathological Response Factors 3303 lead an individual from DisasterTrauma 3301 to Post-Traumatic Stress Disorder 3320. It is the job ofhealthcare personnel to put the individual on a Pathway to Recovery3310. In exemplary embodiments of the present invention ImmunoScoretechnologies can be used to determine possible therapies 3315, as wellas to track immunological correlates of PTSD to verify diagnosis andevaluate therapeutic efficacies.

In the immediate aftermath of a traumatic event, most people experiencea combination of the following symptoms: (a) difficulty sleeping, (b)difficulty concentrating, (c) irritability, (d) nightmares, (e)recurrent thoughts of the trauma, and (f) distress at the reminder ofthe traumatic event. The question in the determination of a pathologicalresponse is when does the continuation of these “normal” responsesbecome pathological, and have serious effect on the health of theindividual's immune system?

There are different possible outcomes of trauma exposure. There is anincreased risk of: (a) Post-Traumatic Stress Disorder (PTSD), (b) majordepression, (c) panic disorder, (d) generalized anxiety disorder, (e)substance abuse, and (f) other somatic symptoms or expressions ofphysical illness including hypertension, asthma, and chronic painsyndromes. The differential outcomes may rely on different physiologicalparameters.

Pre-existing cognitive factors may or may not be the cause, result, orcorrelate of pre-existing biological alterations, either or both settingthe stage for an extreme response to the trauma. Clarifying the precisenature and biological correlates of symptoms that appear in theimmediate aftermath of a trauma will assist in developing models forpotential prophylactic interventions and early treatments. In thisregard the ImmunoScore diagnostic panel could initially be used in aresearch application to track immune system markers and relate them tospecific conditions. As a system database evolves, ImmunoScore panelscan, for example, be used as a guide to therapeutic treatment.

Individuals currently at the greatest risk for developing PTSD followingtrauma are those individuals with (a) a family history ofpsychopathology, (b) a history of childhood abuse, (c) prior traumaexposure, and (d) the cognitive factors of lower IQ, female gender, andpoor social support. There is increased concordance for PTSD inmonozygotic twins compared with dizygotic twins lending support to thegenetic pre-disposition argument of PTSD.

R. Monitor Adoptive Immunotherapy/Transplants

After adaptive transfer, several events must occur for T cells to causethe regression of established tumors. T cells must be activated in vivothrough antigen-specific vaccination. They must then vigorously expandto levels capable of causing the destruction of significant tumorburdens. Finally, anti-tumor T cells must survive long enough tocomplete the eradication of all tumor cells (Overwijk, et al. 2003). Ithas been found in an animal model that the progressive differentiationof T cells to a terminal differentiated effector stage results in aseries of phenotypic and functional changes that make them less “fit” toperform these functions (Gattinoni, et al. 2005).

In patients under consideration for adaptive immunotherapy and/ortransplantation, history and analyses of exposure to CMV, EBV, West NileVirus, and viral hepatitis in both the donor and recipient are crucial.ImmunoScore diagnoses of both the donor and recipient would examine theimmune history of both individuals.

S. Elective Surgery

Many patients opt for elective surgery—plastic surgery, facial plasticsurgery, dermatology, cosmetic dentistry, vision, urology, andinfertility among others. Whenever undergoing surgery, there is a riskof nosocomial infection. Common organisms that cause nosocomialinfections are Apergillus, Candida, Staphylococcus aureus,Staphylococcus epidermidis, Pseudomonas aeruginosa, and Bordetellapertusis. Prior to elective surgery, it would benefit the patient andthe attending surgeon to know the level of antibody protection to theseinfectious agents. An ImmunoScore panel could be tailored to meet thesediagnostic needs and immunizations could be provided to those agentswith available vaccines. If the patient's immune status is sufficientlypoor, a recommendation not to have the surgery may be given. In thesecases, the expected value of costs of complications rising frominfections outweighes the expected value of the benefits from thesurgery. In addition, following surgery patients could be screened for creactive protein (CRP), tumor necrosis factor-alpha (TNF-α), IL-6, andsoluble IL-2 receptor (sIL-2R) as possible early indicators ofinflammation leading to sepsis. It is important to screen for a panel ofanalytes indicating sepsis, as one analyte is often not enough to get aproper diagnosis.

T. Services to Charitable Foundations Promoting Immunological Well Being

Currently, the lack of accurate, affordable, and accessible diagnostictests significantly impedes global health efforts. The Global Alliancefor Vaccines and Immunizations (GAVI) was created in 1999 to protecthealth and save children's lives throughout the widespread use of modernvaccines. GAVI is a partnership of governments, internationalorganizations, major philanthropists, research institutions, and theprivate sector that work together to: (a) improve access to sustainableimmunization services, (b) expand the safe use of all neededcost-effective vaccines, (c) accelerate research and development effortsfor new vaccines needed in developing countries, (d) make immunizationcoverage a key indicator of development, (e) promote sustainability byadequate financing, and (f) reinforce global and national immunizationgoals including eradicating polio, eliminating maternal and neonataltetanus, reducing measles, and increasing access to vitamin A.

Underlying all health care tools—including therapeutic products,vaccines, and other preventative tools—are “platform” technologies thatdefine and facilitate their use. For example, immunochromatography is atechnology platform that has enabled the development of affordable,easy-to-use dipstick format diagnostic tools. The ImmunoScore diagnosticpanel, a platform technology, can be used to great advantage by GAVI toimprove global health efforts

GAVI issues requests for proposal (RFPs) to support research efforts tocreate diagnostic technology platforms and tolls that enable improvedprevention, treatment, and surveillance in developing country settings.The foundation issues RFPs to support the systemic evaluation of sets ofgenes, proteins, and cellular pathways to determine their potential rolein contributing to the development of new vaccines, diagnostics, anddrugs for GAVI's priority diseases and conditions. One area of concernis population genetics and how to design drugs and vaccines todiscourage the emergence of resistance and to discover how geneticsaffects the efficacy of drugs and other interventions. The ImmunoScoredatabase would be an ideal tool for GAVI to use to evaluate geneticparameters and immune response to vaccines and drugs underconsideration. A second area is applied immunology. Here systematicapproaches, such as that provided by the ImmunoScore technology, areneeded to measure the human immune response to guide vaccine design anddefine biological signs that identify early or latent infection.

U. Discovery of Unwanted Immunogenicity of Therapeutics

There is potential of the human immune system to identify biologicaltherapeutic products as foreign and mount an immune response. There arethree main areas of concern with the production of antibodies againstbiological therapeutics in humans:

Safety—assurance of safety involves the assessment of whether antibodiesinduced could have adverse clinical implications.

Efficacy—can be affected by the presence of antibodies binding to theproduct and reducing its potency.

Measurement of phamacokinetic/pharmacodynamic parameters—the presence ofantibodies can alter these clinical parameters and also interfere in theassays used in their assessment (Koren, et al. 2002).

The immunogenicity of therapeutic proteins can be influenced by manyfactors, including the genetic background of the patient, the type ofdisease, the type of protein (human or nonhuman), the presence ofconjugates or fragments, the route of administration, dose frequency,and duration of treatment (Schellekens, 2002). Manufacturing, handling,and storage can introduce contaminants, or alter the three dimensionalstructure of the protein via oxidation or aggregate formation. Variousmeans have been suggested by which therapeutic proteins might bemodified to reduce their immunogenicity, including PEGylation,site-specific mutagenesis, exon shuffling, and humanization ofmonoclonal antibodies (Schellekens, 2002). In the future, it may bepossible to predict the immunogenicity of new therapeutic proteins moreaccurately, using specifically designed animal models, includingnonhuman primates and transgenic mice.

ImmunoScore diagnoses and database storage could be instrumental in thedevelopment of analytical techniques to monitor both the drugs and thepatient population. An individual's tendency to mount an immunechallenge to a protein therapeutic could be revealed prior to initiationof the treatment based upon the patient's ImmunoScore profile. Inaddition, once therapeutic treatment began, ImmunoScore diagnoses anddatabase management could track a patient's immune response to the drug.The drug manufacturers would be able to use the ImmunoScore technologyto conduct clinical trials and also to select an appropriate populationin which to test the drug. Based upon ImmunoScore population data, thedrug could be designated for use based upon the genotype of theindividual being treated.

FIG. 34 depicts an exemplary process flow for an ImmunoScoreimmunogenicity study in exemplary embodiments of the present invention.The exemplary study is directed to immunogenicity of therapeuticproteins.

With reference thereto, at 3401 a prospective patient's immune statuscan be examined to obtain a baseline ImmunoScore. At 3410 patients forwhom treatment would not be advisable (based upon immune systemprofiling) can be identified, and therapeutic treatment for a patientgroup for which therapy is advisable can be initiated. At 3420 patients'further treatment and health care recommendations can be made, based oncareful periodic monitoring of antibody levels to therapeutic proteins.In addition, cellular components of the immune system would warrantcareful monitoring—particularly in regard to the antigenic components ofthe therapeutic compound. At 3430 patient data can be compiled for drugsin clinical trial. Population data can also be compiled to assist indrug design. At 3440, follow-up examinations of patients' immune statuspost-treatment can be implemented and the results stored in a systemdatabase. Further screening of antibody levels and T-cell components ofthe immune system can be implemented for all patients. Finally, at 3450the efficacy of therapies to provide necessary treatment to patients canbe evaluated, and the extent of undesirable immunogenicity can bedetermined.

V. Two-Sided Market Applications

A two-sided market is a market wherein there are two sets (at least) ofcustomers that, in effect, need each other. Each type of customer valuesthe market more if the other type of customer also buys the service.Businesses service such markets by acting as “matchmakers.”

Examples of Two-Sided Markets:

-   -   computer operating systems        -   software developers write applications        -   computer users run applications    -   video game console manufacturers serve        -   video game players        -   video game designers    -   payment card companies        -   consumers        -   merchants

These businesses all produce platforms that make matches between two ormore distinct groups of consumers.

The description of two-sided markets likely came about from payment cardcompanies and legal ramifications of what may have been perceived asmonopolistic business practices, but was actually the demonstration oftwo-sided marketing practices. A key aspect to the business model formost of these industries involves the optimal pricing structure: thedivision of revenues between the two sides of the market that gets bothsides on board. The need for pricing structure as well as pricing leveldistinguishes industries based on a two-sided market from the industriesordinarily studied by economists. In two-sided markets, the product maynot exist at all if the business does not get the pricing structureright. Currently, there is no appreciable market for adult vaccines,other than those for influenza and pneumococcus. ImmunoScore diagnosticscan, for example, likely reveal lapses in protection for vaccinepreventable diseases, such as pertussis, tetanus, diphtheria, mumps,measles, and others. Diagnostic testing can thus reveal a largemarketing potential for vaccine manufacturers.

Both the ImmunoScore diagnostic application and the ImmunoScore databasemanagement modules can be considered as two-sided marketingopportunities, in that none of the participants (patients, insurers,researchers, primary care physicians, vaccine manufacturers, orgovernment entities) may necessarily be willing to enter into abeneficial marketing alliance without direction provided by theImmunoScore platforms, as illustrated in FIG. 35. ImmunoScore can act asa “matchmaker” for these different groups of consumers. An ImmunoScorediagnostic platform can, for example, serve to link patients,physicians, and vaccine manufacturers and help to illustrate the needfor continuing vaccine coverage in adults and children at risk. As anImmunoScore database module grows from an ImmunoScore diagnostic module,insurers, research groups (both academic and commercial), and groupsresponsible for vaccine decision-making (ACIP and AAP) and tracking (CDCand VAERS) can be able to take advantage of the data generated fromassessing the immune status of patients.

Network effects. A network effect arises when the value that one userreceives from a product increases with the number of other users of thatproduct. It goes without saying that the value to governmentaldecision-making, insuring, and research interests can expand enormouslywith the increase in size of an ImmunoScore patient database. Healthinsurers can also be involved at ImmunoScore diagnostic platform level.Insurers that would be interested in providing insurance based upon anindividual patient's ImmunoScore would benefit most from the ImmunoScoredatabase platform. Most network effects arise because a product tends tobe two-sided. ImmunoScore, having more than two interactive sides, woulddemonstrate large network effects that should benefit society as awhole, with better health for the population at large and decreasedcosts for the insurers. Information garnered from an ImmunoScoreDatabase can enable the performance of vaccine researchers and thevaccine decision-makers in the government tremendously.

Survey of Two-Sided Markets Diverse Industries:

-   -   credit cards    -   computer operating systems    -   video games    -   corporate bond trading    -   residential real estate

Firms in these industries have adopted similar business models andpricing strategies for solving the problem they have in common—gettingand keeping two sides of a market on board. The intermediary helpscustomers complete a transaction by providing a platform. Thetransaction occurs when both sides get together. Currently, there is areal need to get adult patients and vaccine manufacturers together forthe betterment of public health. ImmunoScore diagnostics will be aneffective facilitator of this interaction, with the medical insurancecompanies being a third beneficiary. The intermediaries succeed in thebusinesses by figuring out how a pricing structure internalizes theexternalities between the two sides. In the case of ImmunoScoreDiagnostics, the health insurers should be willing to pay for thediagnostic testing as well as the cost of vaccination, as those costswould be less than those to treat debilitating diseases otherwisepreventable by judicious use of vaccination.

A market is two-sided if at any point in time there are:

-   -   two distinct groups of customers—with ImmunoScore diagnostic and        ImmunoScore database platforms, there would be patients, vaccine        manufacturers, health insurers, vaccine researchers, and vaccine        decision-making organizations that would benefit from the        two-sided ImmunoScore platforms.    -   the value obtained by one kind of customer increases with the        number of the other kind of customers—as the number of patients        are added to the database, the database would increase in        potential utility to researchers, vaccine decision-makers,        insurers, and vaccine manufacturers. The more the database        grows, the better it would be for the patient population as        physicians would better be able to determine individual        patient's immune status based on knowledge accumulated over the        entire patient population.    -   the intermediary is necessary for internalizing the        externalities created by one group for the other group—there is        currently no real push for adults or older children to have        diagnostic screening related to vaccine-preventable diseases. As        ImmunoScore data accumulate, there should be an added impetus        for adult and adolescent vaccination coverage.

Researchers have examined the pricing and production strategy of a firmin a two-sided market. Consider the case in which both sides of themarket are buying a “transaction” and in which the seller incurs amarginal cost for consummating that transaction. The prices charged tothe buyers and sellers are two prices. The buyer's demand depends onlyon the price faced by the buyer and the seller's demand depends only onthe price faced by the seller. The demands can be thought of, roughlyspeaking, as the number of buyers and sellers using the system. Thetransactions that a seller engages in, and its benefits from thosetransactions, increase proportionally with the number of buyers on thesystem. The same holds for an individual buyer. Total demand equals theproduct of the two demands. Thus, if there were 500 sellers and 100buyers, there would be 50,000 transactions. The assumption of amultiplicative demand between the two sides actually understates theimportance of the indirect network effects. It ignores the fact that thevalue each side obtains from the other side increases with the number ofcustomers on the other side. In the cases of ImmunoScore diagnostics andan ImmunoScore database, the benefit to all sides of the market couldincrease dramatically (presumably something more than a multiplicativeeffect) as the number of consumers grows. Feeding information to thedatabase can only assist patients, physicians, vaccine decision-makingbodies, vaccine manufacturers, and health insurers.

None of the conditions for determining the price level or the pricestructure in two-sided markets corresponds to marginal revenue equalingmarginal costs on either side of the market. Such conditions have nomeaning in two-sided markets because there is no way to allocate theincreases in revenues from changes in prices to one side or the other.Changes in prices result in more “transactions” from which each sidejointly benefits.

Business Models in Two-Sided Markets. Both sides need to be brought onboard. For instance, there would be no demand by households for paymentcards is they could not be used anywhere, and no demand by retailers ifno one had them to use. Investment and pricing strategies are key togetting both sides on board. Even with both sides on board, businesseshave to carefully balance their two demands. They have to consider howchanges on one side of the market will impact the other side of themarket. The need to balance the needs of the various consumers will beof utmost importance to the careful development of ImmunoScorediagnostics and ImmunoScore database management as two-sided markets.Currently, patients, physicians, and vaccine manufacturers seempainfully unaware of the need for diagnostic testing and boosting forvaccine-preventable diseases.

Getting both sides on board. One way to get both sides on board is toobtain a critical mass of users on one side of the market by giving themthe service for free or even paying them to take it. Another way tosolve the chicken-and-egg problem is to invest in one side of the marketto lower the costs to consumers on that side of participating in themarket. Providing low prices or transfers to one side of the markethelps the platform solve the chicken-and-egg problem by encouraging thebenefited group's participation—which in turn, due to network effects,encourages the non-benefited group's participation. Another effect ofproviding benefits to one side is that this assistance can discourageuse of competing two-sided firms. In the case of the ImmunoScorediagnostic and database platforms, initially the medical insuranceindustry would likely bear the burden of any associated costs, but thebenefit to this industry in increased wellness of their clientele shouldoffset any up-front costs. In addition, to those patients who are seento have an unfavorable ImmunoScore the supplemental insurance industryshould be available and able to come in and insure those individualswith special needs.

Pricing strategies and balancing interests. Firms in mature two-sidedmarkets still have to devise and maintain an optimal pricing structure.In most observed two-sided markets, companies seem to settle on pricingstructures that are heavily skewed towards one side of the market.Certain customers on one side of the market may be extremely valuable tocustomers on the other side of the market —“marquee buyers.” In the caseof the ImmunoScore diagnostic and database platforms, the “marqueebuyers” could be seen as large HMOs that would in truth benefit fromhaving a healthier patient population. The existence of marquee buyerstends to reduce the price to all buyers and increase it to its sellers.Acceptance of ImmunoScore platforms by large insurance organizations andgovernment agencies would enable “bringing on board” smaller insuranceagencies. A similar phenomenon occurs when certain customers areextremely loyal to the two-sided firm—perhaps because of long-termcontracts or sunk-cost investments.

Multihoming. Most two-sided markets in the real world appear to haveseveral competing two-sided firms and at least one side appears tomultihome. Multihoming affects both the price level and the pricingstructure. Not surprisingly the price level tends to be lower withmultihoming. The possibility of multihoming may encourage firms to lowertheir prices on the side of the market in which multihoming could occur.By lowering their prices, firms discourage customers on that side fromaffiliating with other two-sided firms. The firm can then charge more tocustomers on the other side, for whom fewer substitutes are available.

Two-Sided Markets and Social Welfare. A relatively small number of firmstend to compete in two-sided markets. That is because these markets havenetwork effects and usually incur substantial fixed costs for gettingone or both sides on board. Larger firms have advantages over smallerfirms because larger size delivers more value—a bigger network—toconsumers. Firms in concentrated two-sided markets may haveopportunities to earn supra-competitive profits—i.e., profits thatexceed those necessary to attract capital to the industry afteraccounting for risk. Several factors affect the extent to which this canhappen over time.

-   -   7. The extent to which firms are competing to become established        in a two-sided market. This results in investment to court        customers, to provide them with subsidies in the form of        equipment, and to offer them low or negative prices. Vaccine        manufacturers and physicians offices might initially need to be        coaxed into the ImmunoScore diagnostic and database markets, but        should see the benefits as the structure grows.    -   8. The extent to which there are first mover advantages in        getting either side of the market on board Then, the competition        to make these investments should reduce the opportunities to        earn significant supra-competitive returns. Savvy Health        Maintenance Organizations could be the first to realize the        benefits to their coverage that ImmunoScore diagnostics and        database platforms could provide, and as such may be eager to        get into this opportunity at the ground level. The governmental        organizations could also be made to see the benefits of        diagnosing and cataloging lapses in vaccine-preventable disease        conditions.

The consequences of having relatively few competitors in two-sidedmarkets, and the existence of network effects, raise familiar issuesconcerning the efficacy of competitive markets and the possible rolesfor government intervention. The pricing and investment strategies thatfirms in two-sided markets use to get both sides on board and balancethe interests of both sides raise novel questions. These pricing andother business strategies are needed to solve a fundamental economicproblem arising from the interdependency of demand on both sides of themarket. In some cases, the product could not even exist without effortsto subsidize one side of the market or the other. In the case of theImmunoScore platforms, the patients would likely need to be subsidizedby the participation of health insurers.

Researchers have compared the pricing structure adopted by firms intwo-sided markets to the pricing structure that would maximize socialwelfare. Interestingly, they find that a monopoly firm, a firm withcompetition, and a benevolent social planner would adopt similar pricingstructures. The precise relative prices would differ somewhat. Theyfound that the pricing structure adopted by the market is not biasedtowards one side of the market or the other side of the market comparedto the pricing structure that would be adopted by the benevolent socialplanner. ImmunoScore diagnostic and database platforms may be thought ofas a benevolent social plan. The welfare of the patients is paramount,and there would be additional benefits presented to vaccinemanufacturers, research groups, and government organizations.

Two-sided markets are an increasingly important part of the globaleconomy. Firms that provide platforms for multiple consumer groups are acritical part of many interrelated segments of the computer industry. Inmost industrialized countries a large fraction of payments takes placethrough firms and associations that provide platforms for merchants andcustomers to exchange money. The increased importance of the Internetfor household-to-household, business-to-household, andbusiness-to-business transactions and the emergence of e-pay systems onthe Internet will increase the fraction of payments going throughcommercial payment platforms. ImmunoScore diagnostic and databaseplatforms would help bring health care into the 21^(st) century. Thereis a tremendous need for portability in health care record-keeping, andthe ImmunoScore database platform would be instrumental in the transferof health care records from primary care physician to specialist.

Two-sided firms have to come up with the right price structure and theright investment strategy for balancing the demands of the customergroups they must get and keep on their platforms.

In many industries, platforms court two (or more) sides that use theplatform to interact with each other. The platform may chargeinteraction-independent fixed fees. For example, American Expresscharges yearly fees to cardholders. In the case of video games,platforms charge game developers fees for development kits on top ofroyalties per copy sold, and they charge gamers for the console. In thecase of the ImmunoScore database platform, it might be appropriate tocharge academic and commercial research groups for use of theinformation captured by the database modules.

Managers devote considerable time and resources to figure out which sideshould bear the pricing burden, and commonly end up making little moneyon one side (or even using this side as a loss-leader) and recoupingtheir costs on the other side. Marketing managers for the ImmunoScoreplatforms will need to carefully balance many consumers' needs and theapplications of fees.

Pricing Principles for Two-Sided Platforms. Departures from standardbusiness strategies that result from the platform's internalization ofthe other side's welfare (the linkage between the two sides from theplatform's viewpoint). This linkage is most apparent when the platformmakes no or loses money on one side. A factor that is conducive to ahigh price on one side, to the extent that it raises the platform'smargin on that side, tends also to call for a low price on the otherside as attracting members on that other side becomes more profitable.In the case of the ImmunoScore platforms, it is imperative to bringpatients on board, but their participation might be encouraged by thedual factors of their curiosity as to their personal ImmunoScore andalso the participation of their insurer in the platform.

Platforms must perform a balancing act with respect to their pricestructure as well as other policy dimensions; quite generally, theyencourage positive externalities and discourage negative ones and to doso usually constrain one side to the benefit of the other. Whileasymmetric information and the concomitant rent extraction concerns keepthe platform's price structure neutral, it is nonetheless a source ofsub-optimal trade among end-users. The platform has an incentive to capor alter through a subsidy the price charged to buyers so as to boostbuyer's surplus and their willingness to join the platform. Then theplatform behaves pretty much like a public utility commission thataddresses a market power problem by setting a price cap or bysubsidizing some services through a fund levied from other services.

The rationale for constraining the price charged by the seller to thebuyer would vanish if the industry were organized according to thevertical view: were the platform not to deal directly with buyers, theplatform would want to provide sellers with the maximal profit in theirrelationship with buyers and therefore would grant them maximalcommercial freedom. It is only because the platform can extract surpluson the buyer side that it is willing to displease the seller side byconstraining it.

End-users often care not only about the price (that they pay to theplatform and to the other side), but also about the quality of theinteraction. In health care, the quality of the physician-patientinteraction assumes particular importance. An ImmunoScore Diagnosticplatform will help nurture the doctor-patient relationship and focus onthe patient's “wellness” rather than strictly on “treatment.”

While price regulation is complex or inefficient, the platform may stillmake itself attractive to one side of the market by encouragingcompetition on the other side.

Competition on the other side brings prices closer to marginal cost, andthe volume of interactions closer to the efficient volume; it alsoprotects against the hold-up of one's specific investments. AnImmunoScore diagnostic platform could encourage competition amongvaccine manufacturers on behalf of the patient population. Themanufacturers should still realize greater sales, but their pricesshould remain competitive for the insurers and patient population.Accordingly, a two-sided platform benefits from allowing competition ona given side as it can at least partly recoup benefits on the otherside.

Dynamics. To create a two-sided market, a “chicken or egg” problem hasto be solved: to convince some buyers to adopt a certain intermediationplatform, it is necessary to first convince some sellers; but, toconvince the sellers, there must be some buyers on the market. In mostmodels, this problem is avoided by assuming the simultaneous arrival ofagents on the two market sides, in a rational expecitations equilibrium.However, there are circumstances in which one market side has tointervene before the other one. The most cited case is the one ofvideogame consoles which, to get customers, must appear on the marketalready equipped with a complete range of games and complementaryapplications. There appears to be a growing need for the determinationof a patient's immune status. There is a current outbreak of mumpsdisease in the Midwest in individuals that have received two MMRimmunizations. The incidence of pertussis continues to increase. Travelhas now been related to the spread of Severe Acute Respiratory Syndrome(SARS), influenza, measles, tuberculosis, and mumps. The time isappropriate for the introduction of the measurement of the immune statusof individual patients, and the tracking of information regarding eachindividual's immune status. ImmunoScore diagnostic and databaseplatforms can tip the balance from a display of need to a mode of actiongoing forward.

W. Drug Hypersensitivity

Incorporating Drug Hypersensitivity into a Two-Sided Business Model

Adverse drug reactions are common. Identifying true drug allergy,however, can be challenging. Drug hypersensitivity is a clinicaldiagnosis based upon available data. Drug hypersensitivity is defined asan immune-mediated response to a drug agent in a sensitized patient.Identifiable risk factors for drug hypersensitivity reactions includeage, female gender, concurrent illnesses, and previous hypersensitivityto related drugs. Monitoring drug hypersensitivity in patients andincorporating those data into an ImmunoScore database platform isanother example of a two-sided market opportunity. As with the otherexamples of two-sided markets, the medical insurance organizations wouldlikely initially cover most of the expenditures to bring the othermarket components into the market that would be beneficial to allparticipants. Other “sides” of the market would involve patients,physicians, researchers for both the pharmaceutical industry and allergyspecialists.

The Gel and Coombs classification system describes the predominantimmune mechanisms that lead to clinical symptoms of drughypersensitivity (Table 1). This classification system includesIgE-mediated Type I reactions, cytotoxic Type II reactions, Type IIIreactions involving the formation of immune complexes, and the delayed,cell-mediated Type IV reactions. However, some drug hypersensitivityreactions are difficult to classify because of a lack of evidencesupporting a predominant immunologic mechanism. These include certaincutaneous drug reactions and specific drug hypersensitivity syndromes.

Diagnostic testing for these reactions remains somewhat problematic. Thecurrent types of tests and therapeutic considerations are for each ofthe four types of hypersensitivity reactions described in Table 2 below.Confirmation of suspected Type I hypersensitivity reactions requires thedetection of antigen-specific IgE. Currently, skin testing is a usefuldiagnostic procedure for reactivity to penicillin. With other drugagents, a negative skin test does not effectively rule out the presenceof specific IgE. Further IgE tests for other agents await development.The sensitivity of ECL technology as embodied in an exemplaryImmunoScore diagnostic platform can be a very effective tool to enableresearchers to better study IgE populations specific for drug componentantigens. Currently, the diagnosis of drug hypersensitivity is usuallybased upon clinical judgment because definitive, confirmatorydrug-specific testing is often difficult.

Once the diagnosis has been established, appropriate documentationshould be included in the medical record specifying the causative drugand the nature of the adverse effect. Immune-mediated drughypersensitivity reactions typically pose a predictable, more serioushealth risk with re-exposure to the drug. In this application of thetechnology, an exemplary ImmunoScore database platform can, for example,capture all pertinent information related to any adverse drug reaction.This would not only be of benefit to the patient, but also as data wasaccrued, pharmaceutical companies would benefit from statisticalinformation gathered from mining the database. Real drughypersensitivity would also be separated from reactions that may not behypersensitivity. Instead of relying on patient recall and a faulty datacollection system, an exemplary ImmunoScore database can only includedocumented case histories. Patient medications can, for example, betracked via an ImmunoScore database and real hypersensitivity can beofficially documented.

The most important drug-related risk factors for drug hypersensitivityconcern the chemical properties and molecular weight of a drug. Largerdrugs with greater structural complexity are more likely to beimmunogenic. Heterologous antisera, streptokinase and insulin areexamples of complex antigens capable of eliciting hypersensitivityreactions. Another factor affecting the frequency of hypersensitivitydrug reactions is the route of drug administration; topical,intramuscular, and intravenous administrations are more likely to causehypersensitivity reactions. These effects are caused by the efficiencyof antigen presentation in the skin, the adjuvant effects of repositorydrug preparations, and the high concentrations of circulating drugantigen rapidly achieved with intravenous therapy. Oral medications areless likely to result in drug hypersensitivity.

Most medications, because of their small molecular size, are unable toelicit an immune response independently. Drugs must first covalentlybind to larger carrier molecules such as tissue or serum proteins to actas complete multivalent antigens. This process is called haptenation,and the drugs act as haptens. The elicited immune response may behumoral, with the production of specific antibodies, cellular, with thegeneration of specific T lymphocytes, or both. Frequently, the identityof the metabolites is unknown, making it impossible to develop accuratediagnostic tests for drug allergy (Solensky, 2006).

A thorough history is an essential component of the evaluation ofpatients with suspected drug allergies. The history helps guide theclinician in the choice of diagnostic tests and the decision whether itis safe to reintroduce the medication. Typically, years or decades havepassed since reactions occurred, and, as a result, these records areusually unavailable at the time of consultation.

Patients labeled penicillin-allergic are more likely to be treated withmore expensive and broad-spectrum antibiotics, a practice that leads tothe development and spread of multiple drug-resistant bacteria andhigher direct and indirect health care costs. Among patients with areported history of penicillin allergy, 80-90% have no evidence of IgEantibodies to penicillin on skin testing and thus avoid penicillinunnecessarily. The discrepancy between claimed and real penicillinallergies probably results from several factors. The reaction may havebeen predictable or due to the underlying illness and hence may havebeen mislabeled as allergic from the onset. Another contributor to thediscrepancy is the tendency of patients with type 1 penicillin allergyto lose penicillin-specific IgE antibodies over time. Insight into theimmunochemistry of penicillin has allowed for the development ofvalidated skin-test reagents to detect penicillin-specific IgE.

Together with penicillin, cephalosporins are the antibiotics most widelyused for treating common infections, and like penicillin, can causeimmediate reactions. Manifested clinically by urticaria, angio-edema,rhinitis, brochospasm, and anaphylactic shock, such reactions aregenerally IgE-mediated and are among the most dangerous. Although theincidence of severe immediate reactions to cephalosporins does not seemto be much different from that to penicillin, studies of cephalosporinsas allergens are not nearly as numerous or thorough as those onpenicillin, and very few have been dedicated to the still little knowndeterminants responsible for allergic reactions.

Unpredictable adverse reactions to aspirin and NSAIDS fall into severalmajor categories. Respiratory reactions occur in patients withunderlying asthma, non-allergic rhinitis, and nasal polyposis. Thepreferred term for this disorder is aspirin-exacerbated respiratorydisease (AERD). The reactions typically involve the entire respiratorytract, with symptoms of rhinitis, conjunctivitis, and bronchospasm.Patients who have AERD exhibit cross-reactivity with all non-steroidalanti-inflammatory drugs (NSAIDS), but they can tolerate cyclo-oxygenase2 enzyme (COX-2) selective inhibitors. No in vitro tests to detectaspirin sensitivity exist, and oral challenge remains the gold standarddiagnostic test for AERD.

True hypersensitivity reactions to local anesthetics are uncommon andusually consist of delayed contact dermatitis; anaphylaxis from localanesthetics occurs rarely if ever. Most adverse reactions are vasovagal,psychogenic, toxic, or predictable side effects of epinephrine that isoften used in combination with local anesthetics. Large-scale studieshave found that, following full evaluation, virtually all patients witha history of allergy to local anesthetics are able to tolerate thesedrugs. Unfortunately, patients who experience any adverse reaction tolocal anesthetics are frequently labeled allergic and told to avoid all“-caines” in the future. Because evaluation of these patients invariablyfinds them able to receive a local anesthetic, such evaluation preventsthem from being subjected to the increased risk of general anesthesiaor, alternatively, to pain from the absence of anesthesia. Evaluation ofpatients with a supposed allergy to local anesthetics is also importantbecause it serves to alleviate dentists' or physicians' legal(malpractice-related) concerns regarding use of a drug to which thepatient is listed as being allergic.

Allergic drug reactions compose a small percentage of adverse drugreactions, yet they are commonly encountered in clinical practice, andphysicians are taught to routinely question patients about thesereactions during history taking. Medical history taking is critical inthe evaluation of antibiotic allergy and in distinguishing allergicreactions from other adverse reactions. This information is important,since over-diagnosis of allergic reactions can lead to unnecessary useof more costly antimicrobial agents and may promote the development ofresistant microorganisms. Whenever possible, patients who are beingevaluated for possible antibiotic allergy should be encouraged toprovide all medical records related to previous adverse drug reactions.

Treatment. For drugs that are presumed to be mediated by IgE, drugdesensitization my be performed if the implicated agent is required fortreatment. Desensitization involves the administration of increasingamounts of the antibiotic slowly over a period of hours until atherapeutic dose is reached. The mechanism by which clinical toleranceis achieved is unclear, but it is thought to involve antigen-specificmast-cell desensitization. Since maintenance of a desensitized staterequires the continuous presence of the drug, desensitization must berepeated if the antibiotic is required again later.

For reactions that are not considered to be mediated by IgE, managementdepends on the clinical manifestations of the previous reaction. Formacropapular eruptions, the specialist may consider a graded drugchallenge, which is equivalent to provocation testing. Initial startingdoses are generally higher than those used for desensitization, and theinterval between doses varies, ranging from hours to days or weeks. Thedecision whether to discontinue an antibiotic if a reaction occursdepends on the nature of the reaction; bullous lesions or thoseinvolving mucous membranes warrant withdrawal of the drug, whereas itmay be reasonable to treat through milder reactions, such asmaculopapular eruptions, with the use of antihistamines,corticosteroids, or both as needed.

Cephalosporin in patients with penicillin allergy. Penicillins andcephalosporins share a β-lactam ring structure, making cross-reactivitya concern. Whereas most patients who have a history of penicillinallergy will tolerate cephalosporins, indiscriminate administrationcannot be recommended, especially for patients who have hadlife-threatening reactions. For patients with a history of penicillinallergy who require a cephalosporin, treatment depends on whether theprevious reaction was mediated by IgE. If testing is positive and acephalosporin is considered necessary, then desensitization should beperformed with the use of the particular cephalosporin chosen fortreatment.

Areas of Uncertainty. (Gruchalla and Pirmohamed, 2006) The mechanismsunderlying antibiotic allergy have not been clearly elucidated. Thisunderstanding is needed to facilitate the development of betterdiagnostic tools and drugs then are less immunogenic. Betterunderstanding is needed of factors mediating individual susceptibilityto allergic reactions to antibiotics. Some patients have reportedadverse reactions to many chemically unrelated antibiotics. Theexistence of the so-called multiple drug allergy syndrome iscontroversial, and accepted diagnostic tests are needed to document drugallergy in these patients.

Recommendations. Patients who report a history of antibiotic allergyrequire a careful assessment of the nature of the reaction to determineif the likelihood that it was immunologically mediated. For patientswhose history suggests and IgE-mediated reaction to penicillin, skintesting is indicated. If the test results are negative, the β-lactamagent may be administered. If the test results are positive or testingcannot be done, the drug should be avoided or a desensitizationprocedure should be performed.

ImmunoScore and Drug Hypersensitivity. Exemplary ImmunoScore diagnosticand ImmunoScore database platforms can be seen as examples of two-sidedmarkets in both the diagnoses of drug hypersensitivity as well as in theretention of an individual patient's drug hypersensitivity testing andrecords for future health care medication decisions as shown in FIG. 36.In such cases, it would be predicted that the health maintenanceorganizations and pharmaceutical manufacturers (seen at the base of thediagram, propping up the platform structure) would belong to the side(s)of the market most eager to subsidize the other partners. Patients,physicians, and allergy specialists are natural partners to exemplaryImmunoScore diagnostic and database platforms regarding drughypersensitivity. Because the diagnoses of drug hypersensitivityreactions are in their infancy from a scientific standpoint, researchgroups developing diagnostic assays are also natural customers for thetwo ImmunoScore platforms.

Initial patient histories should include a recording of all prescriptionand non-prescription drugs taken within the last month, including datesof administration and dosage. This is a real example of the proposedutility of the ImmunoScore database platform, wherein patientmedications could be tracked and also easily transferable from primarycare physicians to specialists.

For the HMOs and other insurers, drug hypersensitivity diagnoses andcataloging by ImmunoScore are a natural marriage. There are dualconcerns in health care regarding the expense of exotic antibiotics andthe development of antibiotic-resistant strains of organisms. Realpatient information regarding drug hypersensitivity (as opposed topatient recall and limited health records) would certainly be welcomedby the medical and insurance professions.

TABLE 1 Gell and Coombs Classification of Drug HypersensitivityReactions (Riedl and Casillas, 2003) Immune Timing of reaction MechanismClinical manifestations reactions Type I Drug-IgE complex Urticaria,angioedema, Minutes (IgE binding to mast cells bronchospasm, to hoursmediated) with release of pruritus, vomiting, after drug histamine,diarrhea, anaphylaxis exposure inflammatory mediators Type II SpecificIgG or IgM Hemolytic anemia, Variable (cytotoxic) antibodies directed atneutropenia, drug-hapten coated thrombocytopenia cells Type III Tissuedeposition of Serum sickness, fever, 1 to 3 (immune drug-antibody rash,arthralgias, weeks complex) complexes with lymphoadenopathy, after drugcomplement urticaria, exposure activation and glomerulonephritis,inflammation vasculitis Type IV MHC presentation of Allergic contact 2to 7 days (delayed, drug molecules to T dermatitis, after cell- cellswith cytokine maculopapular drug cutaneous mediated) and inflammatoryrash drug mediator release exposure

TABLE 2 Diagnostic Testing and Therapy for Drug Hypersensitivity(Solensky, 2005) Immune reaction Laboratory tests Therapeuticconsiderations Type I Skin testing Discontinue drug (IgE-mediated)Radioallergosorbent test Consider epinephrine, (RAST) antihistamines,systemic Serum trypase corticosteroids, bronchodilators Inpatientmonitoring, if severe Type II Direct or indirect Coombs' Discontinuedrug (cytotoxic) test Consider systemic corticosteroids Transfusion insevere cases Type III Erythrocyte sedimentation Discontinue drug (immunerate (ESR) Consider NSAIDS, complex) Complement studies antihistamines,or systemic Antinuclear antibody, coricosteroids; or antihistoneantibody plasmapheresis, if severe Tissue biopsy for immunofluorescencestudies Type IV Patch testing Discontinue drug (delayed, Lymphocyteproliferation Consider topical cell- assay corticosteroids, mediated)antihistamines, or systemic corticosteroids, if severe

X. Health Care Transparency and Competition

Currently, health care in the United States consumes $2 trillion peryear. Out-of-pocket costs for those who have insurance have nearlytripled in the last six years, and 46 million Americans are uninsured.Unpaid and unpayable health care bills account for the majority of allpersonal bankruptcies in the country. Eight criteria for improvinghealth care can be articulated as:

-   1. Consistent high quality-   2. Lower cost—follows from high quality. Higher quality is often    naturally less expensive. Providers improve quality by honing their    organizational processes to become more efficient and effective—to    avoid error and to do things right the first time.-   3. Available to all—for ethical, political, systemic, and business    reasons, health care must be available to everyone.-   4. Single model—every provider in the system must compete to offer    the best product at the best price.-   5. Shaped by market forces—the consumer market has the sustained    systemic power to bring consumers more for less.-   6. Practical—the solution must arise from present realities.-   7. Progressive—dramatic change can not occur all at once.-   8. Self-reinforcing—as any part of the health care system moves    toward a new reality, that movement must allow and encourage other    parts to move forward as well.

Competition thus far has failed to work the same wonders in health carethat it has in so many other industries. In Redefining Health Care:Creating Value-Based Competition on Results, Michael Porter andElizabeth Teisberg argue that this is because competition has takenplace at the wrong level and over the wrong goals. Further exacerbatingthe problem is the complete absence of feedback loops. Very little inhealth care has a real price or a real measurable result. Competition inhealth care has consisted of health plans' and providers' attempts topush cost and risk of themselves and onto each other or ontoemployers—and now, onto the consumers. Consumers are not looking toembrace an institution, but are looking for a solution for a particularproblem. One can envision a world in which health care is organizedmainly around products tailored to particular medical conditions. Suchproducts can be delivered by medically integrated practice units made upof teams that work together on the same medical condition over longperiods of time. In this particular vision, transparency drives quality.Health plans could steer patients toward the providers who offer thebest results for the least money.

Referring physicians could refuse to recommend any specialist or packagewith quality scores in the lower quintiles, for fear of being sued formalpractice themselves. When health care providers compete at the levelof the medical condition, on real prices and real results, feedbackloops can become extremely compelling. Offering the highest possiblequality at the lowest possible price will no longer be voluntary, andhealth plans will also be forced to compete on the basis of real resultsand genuine customer service at the lowest price, rather than at theircurrent modus operandi—which can include denying coverage and shiftingcost and risk to employers, consumers, and providers.

New structures for public reporting of medical results are popping up onfederal, state and regional levels. In many of these initiatives,process measures are starting to give way to results measures. In anumber of regions, new tiered payment models use co-payments and othermeans to encourage patients to use the providers with the lowest costand highest quality scores. Such models also reward more efficientsystems, those that beat their risk-adjusted cost targets, with higherreimbursements, and punish those less efficient providers with lowerreimbursements.

The pieces—transparency, integrated products, and true measurement—arecoming into play in the health care marketplace. Once it becomes commonfor health care providers to post actual prices and actual results instandardized ways that produce comparable data, it is hard to see howconsumers, insurance companies, and referring physicians would everchoose low quality at high prices.

In exemplary embodiments of the present invention, ImmunoScorediagnostics and database management can, for example,

-   -   keep score not only of patient's immune data, but effectiveness        of treatments/vaccine    -   tie records of physician recommendations relating immune status        to fiscal responsibility and patient well-being    -   provide data for insurers    -   provide data for providers    -   provide data for consumers

Major decisions about health care in the U.S. have traditionally beenmade by employers, who determine for their employees which benefits andforms of coverage are needed, what types of providers are included inthe network, and which organizations administer the benefits. But thispaternalistic approach effectively allowed the consumer to be a passiveparticipant in his or her own health care. The consumer to this pointhas had no economic incentive to seek the best care at the fairestprice, or to give up unhealthy habits. It has been written (Knott, etal. 2007) that new health care formats and competitors are gainingtraction, with MinuteClinics and RediClinics—low cost walk-in healthcare centers for common ailments at one end of the spectrum, and highlypersonalized “concierge care” at the other. In addition, companies thatare not traditional health care players are leveraging theircapabilities to create entirely new offerings that enable and encouragethe move toward health care consumerism. Fidelity, for example, isdeveloping products and tools that exploit the emerging health-wealthintersection, such as a calculator that helps predict out-of-pockethealth care costs. Standardized data on cost, service, and outcomes hasthe power to establish a new basis of competition. Payers are alsopushing for new payment mechanisms, such as pay-for-performance, thatbase reimbursement on outcomes or adherence to broadly accepted clinicalguidelines, known as evidence based medicine.

To make competition and innovation among payers and suppliers possible,an exemplary system could include the following:

-   -   consumers who live healthy lives and plan for their future        health care needs    -   a fundamentally restructured supply side that provides consumers        all the information they need to make wise choices and is quick        to respond to changing consumer demands    -   new kinds of intermediaries to help align the supply and demand        sides and help consumers navigate the complex system

Much of what is needed on the demand side is in place today or likely toemerge in the near term. Consumer-directed health plan (CDHP) enrolleesoffer an early glimpse of subtle changes in a retail market. CDHPenrollees are more likely to be aware of price and quality differencesin products and services and more likely to have seen information andshop around; they are more likely to ask for prices up front, morelikely to negotiate prices, and more willing to trade convenience forlower prices. They are also more likely to plan ahead when making healthcare decisions and to invest dollars now to prevent problems later.

The overall design of the ImmunoScore technology is one in whichpreventative medicine takes the forefront in treatment options. Vaccinestatus is the most obvious application, and patients lacking protectiveantibody levels can be vaccinated. Other levels of immune preparednesswould also be similarly assessed and preventative measures could beundertaken prior to clinical manifestations of autoimmune disease,cancer, or immunosenescence. Similarly, evaluations of physicians andhealth plans could be readily facilitated using the ImmunoScoredatabase. ImmunoScore can be used to discover and define fundamentalrelationships, such as, for example, (i) optimal Th1/Th2 balances, or(ii) lack of any members for immunosenescense or autoimmune disease,that can serve as indicators of overall immune system harmony. Suchrelationships can, for example, be quantified as one or more“ImmunoScores.” Patients with healthy ImmunoScores would point to theirprimary care physicians and their insurers as providers of admirablehealth care practices. Prevention being much more cost-effective thantreatment would provide the best of all worlds to the patients,physicians and insurers. Physicians whose patients had consistentlylower ImmunoScores would raise a cautionary flag and those doctors andpractices could be scrutinized for provision of first rate health care(or something less). If the records were transparent, patients asconsumers would use their dollars to pay for the best possible healthcare rather than pay for poor care at high cost. For example, in 2005,Aetna began testing tools that allow consumers to compare physicians onactual cost, so that they can gauge their out-of-pocket expenses.WellPoint has embarked on a pilot program at the suggestion of GeneralMotors to provide complete comparative cost data for hospitals on“episodes of care.” A number of employers are also finding success withwellness programs. Typical wellness programs feature free or low-costhealth screenings and other sorts of preventive care.

It has been approved proposed that additional investments in healthinformation technology and greater connectivity among providers will beneeded to ease sharing of patient information and enable consumers tobetter manage their own health. ImmunoScore database management can, forexample, serve as an excellent tool to address connectivity issues thatpatients, physicians, and insurers would have. Thus, ImmunoScoretechnology proposes to be a new intermediary in health careconnectivity.

Patients would have more control over their own health caredecisions—spending as well as courses of treatment.

Public health and data collection. In public health, the currentunderlying assumption is that good data will lead to better decisions,which will result in enhanced population health. In practice, nonecessary linear sequence exists from good data to better health(AbouZahr, et al. 2007). Various types of data are obtained at differentlevels of the health system, to be used by several actors for manyreasons. Providers generate and use information in the context ofpatients' care; managers need data to enhance efficiency andeffectiveness; planners rely on statistics for operational decisions;and policymakers use information for prioritization and resourceallocation (AbouZahr, et al. 2007). There are different data sourcescurrently used to formulate public policy—each with advantages anddisadvantages:

-   -   Routinely reported service data. Routine and administrative        reports are generated as a by-product of patient-provider        interactions and health facility functioning. Health facilities        are a primary source of data for notifiable diseases and are at        the heart of a country's surveillance and response programs,        although facility case reporting needs to be complemented by        active case seeking strategies to generate a complete picture of        epidemic risk. No matter how many data elements are routinely        reported, information is inevitably biased by patterns of        service use and non-use, and the extent or direction of bias is        impossible to ascertain without recourse to other sources of        data. Services delivered (number of immunizations, antenatal        visits, outpatients seen, etc) do not necessarily equate to        population need.    -   Population based data. Mistrust of service-based statistics has        fuelled interest in household surveys that can generate unbiased        data for populations as a whole rather than just the sections        that use available health services. These household surveys have        several disadvantages. They need large investments in human and        financial resources and therefore are usually funded externally,        resulting in bias towards the interests of donors or        well-sponsored programs. They are also time-consuming and are        undertaken only occasionally, and generate results spanning a        period, rather than the immediate past. Samples are rarely of        sufficient size to deliver nationally valid results. Growth in        surveys to generate health related data has been fuelled by        their ability to deliver statistics on child mortality,        population coverage, and certain risk factors. In the past few        years, scope for measurement of health status with household        surveys has greatly expanded owing to cheap and reliable        diagnostic tests that can be used in the research setting to        generate population-based estimates of disease prevalence. But        surveys are not as effective for measurement of adult mortality,        which is a relatively rare event compared with child mortality.

ImmunoScore would relieve much of the concern regarding public healthand data collection. As stated above, there has been concern regardingbias in the routinely reported service data. As ImmunoScore grows insize and popular usage, concerns about bias can be alleviated. Servicesdelivered to any individual patient would be based solely upon the needsof that particular patient, and tailored to that individual patient'simmunologic needs, with no regard for social stratum. Politicaljustifications for mis-representation of data would be eliminated by theautomatic and mechanical nature of the data acquisition.

The tremendous requirement for human and financial resources forcollecting population based data would also be alleviated by ImmunoScoretechnology. Data can be collected at hundreds of remote locations andtransferred back to an exemplary ImmunoScore central database. Therewould be no need for third party human resources—data collection wouldoccur at the hospital, clinic, or physician's office and stored forfuture use.

ImmunoScore Tracking of Medical Services (ImmunoScoreKeeping)

As has been described, heath care processes are very complex, involvingboth clinical and administrative tasks, large volumes of data, and alarge number of patients and personnel. Health care processes are alsovery dynamic. As new processes are initiated, changes in health caretreatments, drugs, and protocols can invalidate current methodologies,requiring reparative actions. ImmunoScoreKeeping can, for example,capture all of such complicated dynamic components and provide accurateperformance measurements (“ImmunoScoreCards”) not only for individualpatients, but also as a means of tracking relative efficiencies of othercomplex components of the health care system.

For example, upon a visit to a provider using an ImmunoScore system, thepatient's data can be captured by an “ImmunoScoreKeeper.” (an exemplaryPOC assay reader connected to a system database, as described above).Not only demographic and test data, but also treatments/drugsprescribed, physician's ID number, and insurer can be stored. Anyadditional testing or measurements (blood chemistry, X-rays, physicaltherapy, etc.) can be entered into the remote ImmunoScore datacollection system at, for example, the physician's office. A criticalrequirement for efficient management of health care is the management ofthe quality of service. Appropriate control of quality of service leadsto the creation of quality care services; these, in turn, can fulfillpatient satisfaction.

Traditionally, health care services have been managed using limitedforms of workflow. Some examples of these are clinical andadministrative protocols. However, these protocols have remained limitedin their usefulness in part because developers have rarely incorporatedboth clinical and administrative activities into one comprehensive careprotocol. This lack of integration hinders the delivery of care, as theeffectiveness of protocols is often dependent on many administrativetasks being properly executed at the correct time.

Thus, in exemplary embodiments of the present invention,ImmunoScoreKeeping can enable medical practices to provide betterquality care at reduced costs. ImmunoScore can, for example, maintainkeep ImmunoScoreCards on:

-   -   individual patients    -   physicians    -   groups of physicians/managed care organizations    -   insurers

In addition, as vaccines, drugs, and therapies prescribed can all bemonitored and tracked, an ImmunoScoreKeeper can also monitor theefficacy of the vaccines, drugs, and treatments prescribed. As thesedata are compiled, they can be shared and submitted to appropriateoversight organizations (FDA, CDC, ACIP, Physician's organizations, drugand vaccine manufacturers, etc.) to better enable these groups to makeclear decisions and/or recommendations. Such organizations would beconsumers of ImmunoScore data.

Thus, an ImmunoScoreKeeper can allow insurers to rate physicians andenable their customers (the patients) to make better informed decisionsregarding their choice of physician. An ImmunoScoreKeeper can trackeffectiveness of treatments to patient outcomes. Prescription drug andvaccine efficacies can be monitored not only in population-basedsamplings, but longitudinally in individual patients with repeatedImmunoScore diagnostic testing protocols. Physicians can thus monitorthe efficiency of the practice that they are associated with, andthereby make the best career choices to advance their careers in themost efficient practices. Hospitals could be measured for effectivenessin patient care against other hospitals and groups of physicians. Typesof hospital settings could be evaluated prospectively. Causes ofnosocomial infections might be tracked, for example, to certain types ofhospital environments. Insurers can be measured against common metricsand be forced to compete for business via accurate ImmunoScoreKeeping.

In exemplary embodiments of the present invention, an ImmunoScoreKeepercan, for example, provide a means of integrated monitoring of individualpatient treatment and also administration of both physicians andinsurers practices. The ImmunoScoreKeeper can, for example, generate anumerical value for each component of the health care system upon whichreal competition among providers and insurers could be generated. Thiscompetition would thereby provide substantial increases in health carequality and decreases in health care costs.

Y. User Access Via Data Networks and On-Line Advertising

In exemplary embodiments of the present invention, users can, forexample, access an ImmunoScore Database via computer data networks. Suchnetworks can include, for example, VPN's or the Internet. In exemplaryembodiments of the present invention, an ImmunoScore database can beaccessed, updated and queried via one or more web page portals. With asubstantial ImmunoScore subscriber base of clinicians, health caremanagement professionals, individuals, health insurance managers andexecutives, and pharmaceutical company researchers and managementpersonnel, a given ImmunoScore embodiment can serve as an indispensableportal for anyone involved in the health care, health insurance, lifesciences and related industries. This creates an opportunity fortargeted on-line advertising.

Online advertising is growing quickly. Recent research predicts thatglobal spending on Internet ads will overtake radio in 2008. It has alsobeen predicted that the rate of spending on Internet ads will grow sixtimes faster than that for traditional media between 2006 and 2009, atrend already taking shape in the Middle East and Europe. Even companiesthat do not engage in e-commerce, such as, for example, Unilever,nonetheless want to create better user experiences for people online.They want to improve their brand presence.

Many other companies have started to use Web 2.0 technologies, such as,for example, blogging and video clips, to increase brand awareness. Lastyear (2006) in China, Pepsi invited people to write screenplays forcompany spokesmen and a famous pop singer. And when it launched itsQashqai car in the UK last month, Nissan offered a game website wherepeople could try to shoot the car; it broadcast video clips of the car,which could be linked to blogs and social networking sites; and it ranbanners over some Yahoo sites.

This summer, two companies, Joost and BabelGum, will start to broadcastentire TV programs free over the Internet. The content owners in effecthave their own channels and advertising will pay the way. In effect,“people will watch Friends on a website. We may thus see the death ofthe TV station and the birth of the network station.

Given this state of affairs, in exemplary embodiments of the presentinvention an exemplary system according to the present invention can beused as an Internet portal for everyone associated with health care inthe broadest system (thus encompassing any consumers or providers of anyof the business models described above). In the same sense thatindividuals utilize online search engines, such as, for example, Googleor answer.com, to research a topic, anyone remotely connected to healthcare, can, for example, access an exemplary ImmunoScore webportal.Whether the individual is simply an individual who has records in anImmunoScore database, whether the individual is an executive of a healthinsurance company or an HMO, whether the individual in question is aphysician or hospital administrator, or whether the individual is aresearcher or someone who works in sales or the technical side ofpharmaceutical developments, an exemplary ImmunoScore system and anassociated ImmunoScore web interface can become a ubiquitous tool usedeach and every day by millions of people. Targeted online advertisingcan then be used to deliver business to business, or business toindividual in the case of individual to consumers, advertising to amarket which is already attuned to the benefits of technology which isapplied to healthcare and understands the value of preventive medicine,individualized medicine, and a granular approach to health careprovision and analysis and follow-up as to efficiency and efficacy ofhealth care.

For example, individuals whose ImmunoScore record, after the appropriateanalysis, has disclosed a potential likelihood of having an autoimmunedisease can be provided literature, products, news of new drugs,experimental clinical trials, etc., for their review and potentialparticipation and/or purchase. Similarly, health insurance companiesoffering a healthcare credit exchange program, as described above, canalso advertise such programs and enhancements to such programs to atarget audience of sophisticated health care consumers. An interestedthird party operator of an exemplary system according to the presentinvention could even, for example, offer to create and operate a healthcare exchange program to all health care insurance companies usingImmunoScore! All of these examples list just a few of the possibilities.Thus, the more data that an exemplary ImmunoScore database obtains andlearns how to best process to extract all of the information latenttherein, the greater impact Immunoscore technology can have and thegreater draw an ImmunoScore based web portal can provide.

Z. Prophylatic Therapies During Surgery

Before a surgical procedure, the patient's ImmunoScore can be used toaid in the execution of the surgery. For example, if the patient'sImmunoScore indicates that the patient's immune system is in goodbalance, fewer or less powerful prophylactic antibiotics may beprescribed and/or administered. Benefits include reducing costs, andmaintaining the patient's natural gut flora (which for example, mayavoid Clostridium difficile infections). Conversely, if the patient'simmune system is weak, additional or more powerful antibiotics may behelp prevent a surgically-induced infection. As another example, if thepatient's ImmunoScore indicates substantial allergies, additionaltesting may be useful to determine if the patient is allergic to certainsurgical materials (e.g., latex) so that alternates can be used.

AA. Contraindications for Biological Active Therapeutics

Biologically active therapeutics may present new issues for patients.These therapeutics include priobiotcs. These therapeutics includeviruses that have been engineered to selectively attack only some cellsin the body (e.g., cancerous cells). When administering thesebiologically active therapeutics, there is some chance of unexpectedconsequences. By examining an individual's ImmunoScore, unexpectedconsequences may be avoided by contraindicating the biologically activetherapeutic.

The present invention has been described in connection with exemplaryembodiments and implementations, as examples only. It will be understoodby those having ordinary skill in the pertinent art that modificationsto any of the embodiments may be easily made without materiallydeparting from the scope and spirit of the present invention which isdefined by the appended claims. Such modifications can include, forexample, using other appropriate assays or tests, other rules oranalyses of the results thereof, as may be known in the art to assessthe immune status of individuals or populations. Additionally, suchmodifications can include, for example, using various assay devices andtechniques as may be known, using various available methods of storingand analyzing data (including various “data mining” techniques) as maybe available, and defining various alternative demographic groups andvarious sets of ImmunoScore test panels to be administered thereto.

1. A method of analyzing immunological and other information pertainingto a population of individuals, comprising: (a) establishing a databasecomprising a plurality of records of information each record comprisingone or more fields, where each such field contains: (1) the results ofone or more assays for the presence of a biochemical, or (2)individual-specific information comprising one or more of saidindividual's medical history, doctors' observations of said individualand/or historical, demographic, lifestyle, and familial informationrelating to said individual; (b) segmenting the population bycommonality as to values in one or more of the fields; (c) as to eachsegment: automatically processing the information in said segment of thedatabase to find a first set of correlations between clusters of two ormore fields of records within each segment of individuals in thepopulation; (c) automatically processing each correlation to generate asecond set of correlations; and (d) for each correlation in the secondset: for each field in the cluster, automatically generating a set ofhypotheses or other relevant data that may explain the correlation; and(e) reporting the second set of correlations, their associatedhypotheses and related information to a user.
 2. The method of claim 1,further comprising using the correlations found in (b) and (c) as partof a health care related decision making process.
 3. The method of claim1, wherein said segmentation can include binning a value of a field,where such field has a range of possible values in the database.
 4. Themethod of claim 3, wherein said segmentation includes binning by age, byregion of origin, by country of origin, by fraction of the dynamic rangeof an assay result.
 5. The method of claim 1, wherein said generating aset of hypotheses or other relevant data comprises at least one of asearch of an internal hypothesis database and an external Internetsearch.
 6. The method of claim 1, wherein said processing eachcorrelation to generate a second set of correlations includes furthersegmenting each segmentation by one or more fields in the database toincrease the value of said correlation.
 7. The method of claim 1,wherein said processing each correlation to generate a second set ofcorrelations includes incrementally increasing a minimum threshold ofcorrelation values and tracking the threshold value at which eachcorrelation drops out.
 8. The method of claim 1, wherein said segmentingincludes segmenting by age, a geographical value and sex.
 9. The methodof claim 8, wherein said segmenting further includes binning the agevalue by one of 1, 5, 10 and 20 year bins.
 10. The method of claim 1,further comprising the first set of correlations.
 11. The method ofclaim 9, wherein the data is reprocessed so as to plot each of thesecond set of correlations in a graph of correlation value as a functionof age.
 12. The method of claim 1, wherein the second set ofcorrelations is reported via heat maps for each segment and a listing ofthe correlation value in each cell of each heat map.
 13. The method ofclaim 1, wherein the first and second set of correlations is used togenerate first and second sets of heat maps, and wherein said processingeach correlation includes performing image processing algorithms on thefirst set of heat maps to generate the second set of heat maps.
 14. Themethod of claim 1, wherein the results of one or more assays for thepresence of a biochemical are obtained electronically from an assayreading device, and the individual specific information is obtained fromat least one electronic medical records.
 15. The method of claim 14,wherein the at least one electronic medical records includes a GooglePersonal Health Record.
 16. The method of claim 1, wherein said one ormore assays for the presence of a biochemical comprises a plurality ofcytokine assays.
 17. A method of analyzing an individual's immunologicalstate, comprising: (a) establishing a database comprising a plurality ofrecords of information for a number of individuals, each recordcomprising one or more fields, where each such field contains: (1) theresults of one or more assays for the presence of a biochemical, or (2)individual-specific information comprising one or more of saidindividual's medical history, doctors' observations of said individualand/or historical, demographic, lifestyle, and familial informationrelating to said individual; (b) segmenting the population bycommonality as to values in one or more of the fields; (d) as to eachsegment: automatically processing the information in said segment of thedatabase to find a first set of correlations between clusters of two ormore fields of records within each segment of individuals in thepopulation; (e) automatically processing each correlation to generate asecond set of correlations; and (f) applying a set of rules to theindividual's record to generate health care recommendations andfindings, said findings including whether the individual supports any ofthe first or the second set of correlations found for the individuals ina first and a second segment to which the individual belongs.
 18. Themethod of claim 15, applied collectively to a set of individuals. 19.The method of claim 17, wherein said one or more assays for the presenceof a biochemical comprises a plurality of cytokine assays.
 18. A methodof analyzing information related to the immune status of one or moreindividuals in a population, comprising: (a) establishing a databasecomprising a plurality of records of information each representative ofthe immune status of an individual in the population each of saidrecords including: (i) current information from one or more assays todetermine the immunity of said individual to one or morevaccine-preventable diseases; and (ii) patient-specific informationcomprising one or more of said patient's medical history, said patientsdoctors observations, and/or social, environmental, lifestyle and otherdemographic information relating to said patient; and (b) processing theinformation in said database to find trends or patterns relating to theimmune status of individuals in said patient population; wherein saidprocessing the information in said database includes: generating a listof correlations between variables or fields in the database; for eachcorrelation in the list: generating a set of hypotheses that may explainsaid correlation; and as to each hypothesis in the set, automaticallyanalyzing the data to refute, support or stating that there isinsufficient data to analyze said hypothesis by further processing ofthe database.
 19. A method for analyzing information related to theimmune status of one or more individuals in a population, comprising:(a) establishing a database comprising a plurality of records ofinformation each representative of the immune status of an individual inthe population, to one or more vaccine-preventable diseases, each ofsaid records including (1) current information from one or more assaysto determine the immunity of said individual to one or morevaccine-preventable diseases, and (2) patient-specific informationcomprising one or more of said patients medical history, said patientsdoctors observations and/or demographic information relating to saidpatient; (b) updating said records from time to time with currentinformation as recited in (a)(1) and/or (a)(2); (c) processing theinformation in said database to find trends or patterns relating to theimmune status of individuals in said patient population; (d) modifyingthe said algorithms to reflect the patterns and trends found in step(c); (e) processing the information in an individual's record throughsaid algorithms, and (f) processing the information in said database tofind trends or patterns relating to the immune status of individuals insaid patient population; wherein said processing the information in saiddatabase includes: generating a list of correlations between variablesor fields in the database; for each correlation in the list: generatinga set of hypotheses that may explain said correlation; and as to eachhypothesis in the set, automatically analyzing the data to refute,support or stating that there is insufficient data to analyze saidhypothesis by further processing of the database.
 20. A method forgenerating recommendations for vaccinating one or more individuals in apatient population, comprising: (a) establishing a database comprising aplurality of records of information each representative of the immunestatus of an individual in the population, to one or morevaccine-preventable diseases, each of said records including (i) currentinformation from one or more assays to determine the immunity of saidindividual to one or more vaccine-preventable diseases, and (ii)patient-specific information comprising one or more of said patient'smedical history, said patients doctors observations, and/or demographicinformation relating to said patient; (b) updating said database fromtime to time with current information; (c) providing one or morealgorithms to determine whether or not to vaccinate said individualsbased upon an assay result for antibodies for said one or morevaccine-preventable diseases and other defined factors; (d) processingthe information in said database to find trends or patterns relating tothe immune status of individuals in said patient population to saidvaccine preventable disease; (e) incorporating information comprisingsaid patterns or trends into one or more of said algorithms; (f)processing the information in an individual's record through one or moreof said algorithms, and (g) thereby generating a recommendation forvaccinating said individual; wherein said processing the information insaid database includes: generating a list of correlations betweenvariables or fields in the database; for each correlation in the list:generating a set of hypotheses that may explain said correlation; and asto each hypothesis in the set, automatically analyzing the data torefute, support or stating that there is insufficient data to analyzesaid hypothesis by further processing of the database.
 21. A method ofoptimizing the management of health care for an individual in apopulation, comprising: examining the individual's immune status;identifying diseases that the insured may be susceptible to; calculatingthe risk of contraction for each disease; identifying all prophylactictherapies that could prevent each identified disease; calculating, forall possible combinations of diseases and prophylactic therapies,expected costs of treatment and costs of associated prophylactictherapies; and requiring prophylactic therapies optimized for overallcost, wherein at least one of said examining immune status andidentifying diseases that the individual may be susceptible to includes:(a) establishing a database comprising a plurality of records ofinformation each representative of the immune status of an individual inthe population, to one or more vaccine-preventable diseases, each ofsaid records including (1) current information from one or more assaysto determine the immunity of said individual to one or morevaccine-preventable diseases, and (2) patient-specific informationcomprising one or more of said patient's medical history, said patient'sdoctors observations and/or demographic information relating to saidpatient; (b) processing the information in said database to find trendsor patterns relating to the immune status of individuals in said patientpopulation; and (c) using the said trends or patterns found in (b) indeciding whether or not to vaccinate an individual. wherein saidprocessing the information in said database includes: generating a listof correlations between variables or fields in the database; for eachcorrelation in the list: generating a set of hypotheses that may explainsaid correlation; and as to each hypothesis in the set, automaticallyrefuting, supporting or stating that there is insufficient data toanalyze said hypothesis by further processing of the database.
 22. Themethod of claim 21, further comprising assessing, as a condition ofcontinued coverage, an additional premium charge if the overall costplaces the insured into a higher risk bin.
 23. The method of claim 22,wherein a debit that is exchangeable on a health care credit/debitexchange is issued in lieu of an additional premium.
 24. A system,comprising: at least one local assay device; a central processorconnected to an input/output device; a system database; a hypothesisdatabase; a rules database; and a data network connecting the localassay devices and the central processor; wherein in operationimmunologic and other data relative to a plurality of individuals isobtained at the assay devices and sent to the system database forstorage, and wherein the central processor accesses said data andfirstly processes said data to find correlations between variables orfields in the database across many individuals, and secondly processessaid correlations via rules stored in said rules database to generate aset of hypotheses from those stored in said hypothesis database, andthirdly processes said hypotheses and said data to confirm, exclude orstate as inconclusive each of said hypotheses for one or more of saidcorrelations.
 25. The method of claim 1, wherein a first assay panelcontaining a plurality of cytokine assays is administered, and based onautomatic analyses of the cytokine data, the assays comprising thedatabase records are chosen.
 26. The method of claim 17, wherein a firstassay panel containing a plurality of cytokine assays is administered,and based on automatic analyses of the cytokine data, the assayscomprising the database records are chosen.
 27. A computer programproduct comprising a computer usable medium having computer readableprogram code means embodied therein, the computer readable program codemeans in said computer program product comprising means for causing acomputer to: establish a database comprising a plurality of records ofinformation each record comprising one or more fields, where each suchfield contains: (1) the results of one or more assays for the presenceof a biochemical, or (2) individual-specific information comprising oneor more of said individual's medical history, doctors' observations ofsaid individual and/or historical, demographic, lifestyle, and familialinformation relating to said individual; (b) segment the population bycommonality as to values in one or more of the fields; (c) as to eachsegment: automatically process the information in said segment of thedatabase to find a first set of correlations between clusters of two ormore fields of records within each segment of individuals in thepopulation; (c) automatically process each correlation to generate asecond set of correlations; and (d) for each correlation in the secondset: for each field in the cluster, automatically generating a set ofhypotheses or other relevant data that may explain the correlation; and(e) reporting the second set of correlations, their associatedhypotheses and related information to a user.
 28. A computer programproduct comprising a computer usable medium having computer readableprogram code means embodied therein, the computer readable program codemeans in said computer program product comprising means for causing acomputer to: examine the individual's immune status; identify diseasesthat the insured may be susceptible to; calculate the risk ofcontraction for each disease; identify all prophylactic therapies thatcould prevent each identified disease; calculate, for all possiblecombinations of diseases and prophylactic therapies, expected costs oftreatment and costs of associated prophylactic therapies; and requireprophylactic therapies optimized for overall cost, wherein at least oneof said examine immune status and identify diseases that the individualmay be susceptible to includes: (a) establishing a database comprising aplurality of records of information each representative of the immunestatus of an individual in the population, to one or morevaccine-preventable diseases, each of said records including (1) currentinformation from one or more assays to determine the immunity of saidindividual to one or more vaccine-preventable diseases, and (2)patient-specific information comprising one or more of said patient'smedical history, said patient's doctors observations and/or demographicinformation relating to said patient; (b) processing the information insaid database to find trends or patterns relating to the immune statusof individuals in said patient population; and (c) using the said trendsor patterns found in (b) in deciding whether or not to vaccinate anindividual. wherein said processing the information in said databaseincludes: generating a list of correlations between variables or fieldsin the database; for each correlation in the list: generating a set ofhypotheses that may explain said correlation; and as to each hypothesisin the set, automatically refuting, supporting or stating that there isinsufficient data to analyze said hypothesis by further processing ofthe database.